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MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
MLSS 2015 Sydney
Machine Learning for
Recommender Systems
Alexandros Karatzoglou
Senior Research Scientist @ Telefonica Research, Barcelona
alexk@tid.es
@alexk_z
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Telefonica Research
Machine Learning
Recommender Systems
HCI
Mobile Computing
Systems
Networks
http://www.tid.es
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Tuenti
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Mozilla FirefoxOS Marketplace
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Recent Publications
SIGIR 2014: Gaussian Process Factorization Machines for Context-aware Recommendation
RecSys 2014: Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems
RecSys 2014: Question Recommendation for Collaborative Question Answering Systems with RankSLDA
CIKM 2013: GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains
RecSys 2013: xCLiMF: Optimizing Expected Reciprocal Rank for Data with Multiple Levels of Relevance
ECML/PKDD 2013: Socially Enabled Preference Learning from Implicit Feedback Data
CIKM 2012: Climbing the App Wall: Enabling Mobile App Discovery through Context-Aware Recommendations
RecSys 2012: CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering * Best Paper Award
SIGIR 2012: TFMAP: Optimizing MAP for Top-N Context-aware Recommendation
RecSys 2011: Collaborative Temporal Order Modeling
RecSys 2011: Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping
RecSys 2010: Multiverse Recommendation: N-dimensional Tensor Factorization for Context-Aware Collaborative Filtering
EC-Web 2010: Quantile Matrix Factorization for Collaborative Filtering
AISTATS 2010: Collaborative Filtering on a Budget
RecSys 2009: Maximum Margin Code Recommendation
RecSys 2008: Adaptive Collaborative Filtering
Machine Learning Journal, 2008: Improving Maximum Margin Matrix Factorization * Best Machine Learning Paper Award at ECML PKDD 2008
NIPS 2007: CoFiRank - Maximum Margin Matrix Factorization for Collaborative Ranking
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1.Introduction: What is a Recommender
System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Ranking
5. Diversity
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
From Search to Recommendation
“The Web is leaving the era of search and
entering one of discovery. What's the difference?
SearchSearch is what you do when you're looking for
something. DiscoveryDiscovery is when something
wonderful that you didn't know existed, or didn't
know how to ask for, finds you.” – CNN Money, “The race
to create a 'smart' Google
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
The value of recommendations
Netflix: 2/3 of the movies watched are recommended
Google News: recommendations generate 38% more
click-throughs
Amazon: 35% sales from recommendations
Choicestream: 28% of the people would buy more
music if they found what they liked.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
The “Recommender problem”
Estimate a utility functionutility function
to predictpredict how
a user will likelike an item.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
The “Recommender problem”
C:= {users}
S:= {recommendable items}
u:= utility function, measures the usefulness of
item s to user c,
u : C X S→ R
where R:= {recommended items}.
For each user c, we want to choose the items s
that maximize u.
∀c∈C , s'c=argmax (u(c , s)) s∈S
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
A good recommendation
is relevant to the user: personalized
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
A good recommendation
is diverse:
it represents all the possible interests of one user
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
A good recommendation
Does not recommend items the user already
knows or would have found anyway.
Expands the user's taste into neighboring areas.
SerendipitySerendipity = Unsought finding= Unsought finding
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Top k recommendations
Usually users take into account only few
suggestions. There is a need to do better on the
top scoring recommended items
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Ranking
5. Diversity
3. Research Directions
4. Conclusions
5. References
Alexandros Karatzoglou – September 06, 2013 – Recommender Systems
Approaches to Recommendation
Recommendations are based on:
Users' past behaviour (Collaborative Filtering – CF):
Find similar users to me and recommend what they
liked (Memory-based)
Model taste patterns in large amounts of user-item
interactions (Model-based).
Item features (Content-based)
Users' features (Demographic)
Social recommendations (Trust-based)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
What works?
Depends on the domain and particular problem
Currently, the best approach is Collaborative Filtering.
Other approaches can be combined to improve results
What matters?
Data preprocessing: outlier removal, denoising, removal of
global effects
Combining methods
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2.1. Collaborative Filtering
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Collaborative Filtering
The task of predictingpredicting (filtering) user
preferences on new items by collectingcollecting taste
information from many users (collaborative).
Challenges:
many items to choose from
very few recommendations to propose
few data per user
no data for new user
very large datasets
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: User-based CF
Target (or Active)
user for whom the
CF
recommendation
task is performed
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
2
4
1
Example
Each user has expressed
an opinion for some
items:
Explicit opinion:
rating score
Implicit: purchase
records or listen to
tracks
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: User-based CF
1. Identify set of
items rated by the
target user
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: User-based CF
1. Identify set of
items rated by
the target user
2. Identify which
other users rated
1+ items in this set
(neighbourhood
formation)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
3. Compute how similar
each neighbour is to the
target user (similarity
function)
4. In case, select k most
similar neighbours
User-based Similarity
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
5. Predict ratings for the target user's unrated items
(prediction function)
6. Recommend to the target user the top N products
based on the predicted ratings
User-based CF
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
User-based CF
Target user u, ratingsratings matrix Y
yv,i → rating by user v for item i
Similarity Pearson r correlation sim(u,v) between users u &
v
Predicted rating
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
sim(u,v)
NA
NA
Example: User-based CF
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: User-based CF
0.87
sim(u,v)
NA
NA
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
1
sim(u,v)
0.87
NA
NA
Example: User-based CF
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
-1
0.87
1
sim(u,v)
NA
NA
Example: User-based CF
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
3.51*
4
5
4
4
1
3.81*
5
5
4
2.42*
1
2
5
2.48*
4
1
2
-1
0.87
1
sim(u,v)
NA
NA
Example: User-based CF
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: Item-based CF
Target item:
item for
which the
CF
prediction
task is
performed.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Item-based CF
The basic steps:
Identify set of users who rated the target item i
Identify which other items (neighbours) were rated
by the users set
Compute similarity between each neighbour &
target item (similarity function)
In case, select k most similar neighbours
Predict ratings for the target item (prediction
function)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Item Based Similarity
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Item Based Similarity
Target item I
yu,j → rating of user u for item j, average rating for j.
Similarity sim(i,j) between items i and j (Pearson-
correlation)
Predicted rating
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: Item-based CF
-1sim(i,j)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
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5
4
5
4
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1
5
5
4
1
2
5
4
1
2
Example: Item-based CF
-1-1sim(i,j)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: Item-based CF
0.86-1-1sim(i,j)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: Item-based CF
1-1-1 0.86sim(i,j)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
1
2
Example: Item-based CF
sim(6,5) cannot
be calculated
1-1-1 0.86sim(i,j) NA
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
5
4
5
4
4
1
5
5
4
1
2
5
4
2.48*
2.94*
1
2
1.12*
Example: Item-based CF
1-1-1 0.86sim(i,j) NA
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
PearsonPearson r correlation-based Similarityr correlation-based Similarity
does not account for user rating biases
Cosine-basedCosine-based SimilaritySimilarity
does not account for user rating biases
AdjustedAdjusted Cosine SimilarityCosine Similarity
takes care of user rating biases as each pair in the co-rated
set corresponds to a different user.
Item Similarity Computation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Performance Implications
Bottleneck: Similarity computation.
Time complexity, highly time consuming with millions
of users & items in the database.
Two-step process:
“off-line component” / “model”:
similarity computation, precomputed & stored.
“on-line component”: prediction process.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Two-step process
OfflineOffline OnlineOnline
Learning
Process
Model
Or
Clusters
Decision
Process
Context
Recommended
Items
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Performance Implications
User-based similarity is more dynamic.
Precomputing user neighbourhood can lead to
poor predictions.
Item-based similarity is static.
We can precompute item neighbourhood.
Online computation of the predicted ratings.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Memory based CF
+ Requires minimal knowledge engineering efforts
+ Users and products are symbols without any internal
structure or characteristics
+ Produces good-enough results in most cases
- Requires a large number of explicit and reliable
“ratings”
- Requires standardized products: users should have
bought exactly the same product
- Assumes that prior behaviour determines current
behaviour without taking into account “contextual”
knowledge
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
The Sparsity Problem
Typically large product sets & few user ratings
e.g. Amazon:
in a catalogue of 1 million books, the probability that
two users who bought 100 books each, have a book
in common is 0.01
in a catalogue of 10 million books, the probability
that two users who bought 50 books each, have a
book in common is 0.0002
CF must have a number of users ~ 10% of the
product catalogue size
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
The Sparsity Problem
Methods for dimensionality reduction
Matrix Factorization
Clustering
Projection (Principal Component Analysis ...)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Personalised vs Non-Personalised CF
CF recommendations are personalized: the prediction
is based on the ratings expressed by similar users;
neighbours are different for each target user
A non-personalized collaborative-based
recommendation can be generated by averaging the
recommendations of ALL users
How would the two approaches compare?
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Personalised vs Non-Personalised CF
0,1510,2230,0222811718164974424EachMovie
0,1790,2330,041100020939526040MovieLens
0,1520,2200,725351944910048483Jester
MAE
Pers
MAE
Non
Pers
density
total
ratings
itemsusersData Set
Not much difference indeed!
vij is the rating of user i for product j
and vj is the average rating for
product j
MAENP =
∑i, j
∣vij−v j∣
num.ratings
Mean Average Error Non Personalized:
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Model-Based
Collaborative Filtering
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Model Based CF Algorithms
Models are learned from the underlying data rather
than heuristics.
Develop a model of user ratings:
Bayesian network (probabilistic)
Clustering (classification)
Association rules
Other models:
linear regression,
probabilistic latent semantic analysis ...
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Matrix Factorization
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d
d
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning: Stochastic Gradient
Descent
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning: Stochastic Gradient
Descent
For each rating of item j by user i:
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Matrix Factorization
2
5
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5
4
4
1
5
5
4
1
2
5
2
4
1
d
d
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Objective Function
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Loss Functions for MF
Squared error loss:
Mean Average Error:
Binary Hinge loss:
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Restricted Boltzmann Machines
A (generative stochastic) Neural Network
Learns a probability distribution over its
inputs
Used in dimensionality reduction, CF, topic
modeling, feature learning
Essential components of Deep Learning
methods (DBN's, DBM's)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Restricted Boltzmann Machines
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Restricted Boltzmann Machines
Each unit is in a state which can be active or
not active.
Each input of a unit is associated to a weight
The transfer function Σ calculates for each unit a
score based on the weighted sum of the inputs
This score is passed to the activation function φ
which calculated the probability that the unit
state is active.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to Rank
- Usually we care to make accurate ranking and not
rating prediction
- Square loss optimizes to accurately predict 1s and
5s.
- RS should get the top items right -> Ranking problem
- Why not to learn how to rank directly
- Learning to Rank methods provide up to 30%
performance improvements in off-line evaluation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Restricted Boltzmann Machines
Each unit in the visible layer vi
corresponds to one item
The number of the hidden units hj
is a parameter.
Each vi
is connected to each hj
through a weight wij
In the trainingtraining phase, for each user:
if the user purchased the item the corresponding vi
is activated.
The activation states of all vi
are the input of each hj
Based on this input the activation state of each hj
is calculated
The activation state of all hj
become now the input of each vi
The activation state of each vi
is recalculated
For each vi
the difference between the present activation state
and the previous is used to update the weights wij
and thresholds θj
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Restricted Boltzmann Machines
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Restricted Boltzmann Machines
In the predictionprediction phase, using a trained RBM, when recommending to a user:
For the items of the user the corresponding vi
is activated.
The activation states of all vi
are the input of each hj
Based on this input the activation state of each hj
is calculated
The activation state of all hj
become now the input of each vi
The activation state of each vi
is recalculated
The activation probabilities are used to recommend items
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Recurrent Neural Networks for CF
RNN's are Neural Networks designed to model sequences
RNN for CF tries to predict the next item given all previous ones
e.g. predict the next song, video, given the history of streams.
RNN's model the current output as a function of the
previous output and a hidden (latent) state
RNN models the items
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Recurrent Neural Networks for CF
h0
y0
h1
y1
h2
y2
...
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Recurrent Neural Networks for CF
2 vectors of dimension k:
time steps: 0... t-1.
“hidden” internal states: h0
... ht-1
.
Output vectors y
At each time step:
Predict the output yi
given the hidden state hi
(we need to model P(yi
|hi
))
Feed yi
back into the next hidden state hi+1
:
hi+1
= f(a(hi
) + b(yi
)).
where f is generally some nonlinear function (e.g. sigmoid or tanh),
a, b are usually a simple linear transform.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RNN for CF: example
Predict a sequence (vector yi
) of items:
Assume P(yi
|hi
) is a normal distribution
the log-likelihood of the loss is the (negative) L2 loss: -(yt
– ht
)2
hi+1
= tanh(Uyi
+ Vhi
) & h0
= 0.
yi
= W hi
Learn the k2
unknown parameters (U, V, and W) that
maximize the log-likelihood over all:
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RNN for CF: example
Maximize the log-likelihood through back-propagation.
we need to do two steps through each sequence.
First propagation: For i=0... t-1: Calculate all h_i.
the total log-likelihood is the sum of all log-probabilities of
observing each output:
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RNN for CF: example
Define the partial derivatives of the log-L with respect to the hidden state:
Since each hi
only influences later hidden states,
is just a function of all future j = i... t-1.
We can rewrite as a function of :
We can evaluate this backwards from t-1...0.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RNN for CF: example
P(yi
| hi
) and are specified by our model
Derive the gradients for the unknown parameter U:
Derive similar gradients for V, W.
Optimize using SGD over lots of samples.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RNN for CF
Given a sequence of watched movies, predict the next one.
Now yi
is not a scalar, but one out of many items.
We need a probability distribution P(yi
|hi
).
The Softmax distribution over all possible outputs.
This means we have to learn a vector aj
for each item.
P(yi
|hi
) is now proportional to :
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RNN for CF
We also need something linking back the output to the next hidden state.
we will learn another set of vectors bj
– one for each item.
we don’t need the matrices U and V. Our model now becomes:
 
Again, we maximize the total log-likelihood
Note that we have the unknowns aj
‘s and bj
‘s for each item. [Erik Bernhardsson
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Clustering
ClusterCluster customers into categories based
on preferences & past purchases
ComputeCompute recommendations at the
cluster level:
all customers within a cluster receive the
same recommendations
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Clustering
B, C & D form 1 CLUSTER vs. A & E form another cluster.
« Typical » preferences for CLUSTER are:
Book 2, very high
Book 3, high
Books 5 & 6, may be recommended
(Books 1 & 4, not recommended)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Clustering
Customer F is classified as a new member of CLUSTER
will receive recommendations based on the CLUSTER's
preferences :
Book 2 will be highly recommended to Customer F
Book 6 will also be recommended to some extent
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Clustering
+ It can also be applied for selecting the k
most relevant neighbours in a CF algorithm
+ Faster: recommendations are per cluster
- less personalized: recommendations are
per cluster vs. in CF they are per user
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Association rules
Past purchases used to find relationships of
common purchases
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Association rules
+ Fast to implement
+ Fast to execute
+ Not much storage space required
+ Not « individual » specific
+ Very successful in broad applications for large
populations, such as shelf layout in retail stores
- Not suitable if preferences change rapidly
- Rules can be used only when enough data validates
them. False associations can arise
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Limitations of CF
ColdCold Start:
It needs to have enough users in the system.
New items need to get enough ratings.
Sparsity:
it is hard to find users who rated the same items.
Popularity Bias:
Cannot recommend items to users with unique tastes.
Tends to recommend popular items.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Cold-start
New User ProblemNew User Problem: the system must first learn the
user’s preferences from the ratings.
Hybrid RS, which combines content-based
and collaborative techniques, can help.
New Item ProblemNew Item Problem: Until the new item is rated by
a substantial number of users, the RS is not able
to recommend it.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Content-Based
Recommendations
Recommendations are based on the
information on the contentcontent of itemsof items rather than
on other users’ opinions.
Use a machine learning algorithm to model
the users' preferences from examples based on
a description of the content.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
What is content of an item?
Explicit attributes or characteristics
e.g. for a movie:
Genre: Action / adventure
Feature: Bruce Willis
Year: 1995
Textual content
e.g. for a book:
title,
description,
table of content
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
In Content-Based
Recommendations...
The recommended items for a user are based on the
profile built up by analysing the content of the items
the user has liked in the past
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Content-Based
Recommendation
Suitable for text-based products (web pages, books)
Items are “described” by their features (e.g. keywords)
Users are described by the keywords in the items they
bought
Recommendations based on the match between the
content (item keywords) and user keywords
The user model can also be a classifier (Neural
Networks, SVM, Naïve Bayes...)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Advantages of CB Approach
+ No need for data on other users.
+ No cold-start or sparsity problems.
+ Can recommend to users with unique tastes.
+ Can recommend new and unpopular items
+ No first-rater problem.
+ Can provide explanations of recommended
items by listing content-features that caused an
item to be recommended.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Disadvantages of CB Approach
- Only for content that can be encoded as meaningful
features.
- Some types of items (e.g. movies, music)are not amenable
to easy feature extraction methods
- Even for texts, IR techniques cannot consider multimedia
information, aesthetic qualities, download time: a positive
rating could be not related to the presence of certain
keywords
- Users’ tastes must be represented as a learnable function of
these content features.
- Hard to exploit quality judgements of other users.
- Difficult to implement serendipity
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Content-based Methods
Content(s):= item profile, i.e. a set of
attributes/keywords characterizing item s.
weight wij measures the 'Importance” (or
“informativeness”) of word kj in document dj
term frequency/inverse document
frequency(TF-IDF) is a popular weighting
technique in IR
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Content-based User Profile
ContentBasedProfile(c):= profile of user c
profiles are obtained by:
analysing the content of the previous items
using keyword analysis techniques
e.g., ContentBasedProfile(c):=(wc1, . . . , wck)
a vector of weights, where wci denotes the importance of
keyword ki to user c
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Similarity Measurements
In content-based systems, the utility function
u(c,s) is defined as:
where ContentBasedProfile(c) of user c and
Content(s) of document s are both
represented as TF-IDF vectors of keyword
weights.
uc ,s=scoreContentBasedProfile c ,Content s
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Similarity Measurements
Utility function u(c,s) usually represented by some
scoring heuristic defined in terms of vectors, such
as the cosine similarity measure.
uc ,s=cos wc , ws=
wc⋅w s
∥ wc∥2×∥ ws∥2
=
∑i=1
K
wi , c wi , s
∑i=1
K
wi , c
2
∑i=1
K
wi , s
2
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Content-based Recommendation.
An (unrealistic) example
How to compute recommendations of books based onlyon
their title?
A customer buys the book: Building data mining applications for CRM
7 Books are possible candidates for a recommendation:
Accelerating Customer Relationships: Using CRM and Relationship Technologies
Mastering Data Mining: The Art and Science of Customer Relationship Management
Data Mining Your Website
Introduction to marketing
Consumer behaviour
Marketing research, a handbook
Customer knowledge management
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
building data
mining
applications for
crm
Accelerating
Customer
Relationships:
Using CRM and
Relationship
Technologies
Mastering Data
Mining: The Art
and Science of
Customer
Relationship
Management
Data Mining Your
Website
Introduction to
marketing
consumer
behavior
marketing
research, a
handbook
customer
knowledge
management
a 1
accelerating 1
and 1 1
application 1
art 1
behavior 1
building 1
consumer 1
crm 1 1
customer 1 1 1
data 1 1 1
for 1
handbook 1
introduction 1
knowledge 1
management 1 1
marketing 1 1
mastering 1
mining 1 1 1
of 1
relationship 2 1
research 1
science 1
technology 1
the 1
to 1
using 1
website 1
your 1
COUNT
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Content-based
Recommendation
Computes distances between this book & all others
Recommends the « closest » books:
#1: Data Mining Your Website
#2: Accelerating Customer Relationships: Using CRM and Relationship
Technologies
#3: Mastering Data Mining: The Art and Science of Customer
Relationship Management
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
building data
mining
applications for
crm
Accelerating
Customer
Relationships:
Using CRM and
Relationship
Technologies
Mastering Data
Mining: The Art
and Science of
Customer
Relationship
Management
Data Mining Your
Website
Introduction to
marketing
consumer
behavior
marketing
research, a
handbook
customer
knowledge
management
a 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.000
accelerating 0.000 0.432 0.000 0.000 0.000 0.000 0.000 0.000
and 0.000 0.296 0.256 0.000 0.000 0.000 0.000 0.000
application 0.502 0.000 0.000 0.000 0.000 0.000 0.000 0.000
art 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000
behavior 0.000 0.000 0.000 0.000 0.000 0.707 0.000 0.000
building 0.502 0.000 0.000 0.000 0.000 0.000 0.000 0.000
consumer 0.000 0.000 0.000 0.000 0.000 0.707 0.000 0.000
crm 0.344 0.296 0.000 0.000 0.000 0.000 0.000 0.000
customer 0.000 0.216 0.187 0.000 0.000 0.000 0.000 0.381
data 0.251 0.000 0.187 0.316 0.000 0.000 0.000 0.000
for 0.502 0.000 0.000 0.000 0.000 0.000 0.000 0.000
handbook 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.000
introduction 0.000 0.000 0.000 0.000 0.636 0.000 0.000 0.000
knowledge 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.763
management 0.000 0.000 0.256 0.000 0.000 0.000 0.000 0.522
marketing 0.000 0.000 0.000 0.000 0.436 0.000 0.368 0.000
mastering 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000
mining 0.251 0.000 0.187 0.316 0.000 0.000 0.000 0.000
of 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000
relationship 0.000 0.468 0.256 0.000 0.000 0.000 0.000 0.000
research 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.000
science 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000
technology 0.000 0.432 0.000 0.000 0.000 0.000 0.000 0.000
the 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000
to 0.000 0.000 0.000 0.000 0.636 0.000 0.000 0.000
using 0.000 0.432 0.000 0.000 0.000 0.000 0.000 0.000
website 0.000 0.000 0.000 0.632 0.000 0.000 0.000 0.000
your 0.000 0.000 0.000 0.632 0.000 0.000 0.000 0.000
TFIDF Normed Vectors
Data
0.187 0.316
Data mining
your website
Mastering Data Mining:
The Art and Science
of Customer Relationship
Management
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context
Context is a dynamic set of
factors describing the state
of the user at the moment of
the user's experience
Context factors can rapidly
change and affect how the
user perceives an item
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context in Recommendations
Temporal: Time of the day, weekday/end
Spatial: Location, Home, Work etc.
Social: with Friends, Family
Recommendations should be tailored to the
user & to the current Context of the user
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Level of Adaptation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context-Aware RS: Pre-filtering
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context-Aware RS: Post-filtering
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context-Aware RS:
Tensor Factorization
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Factorization Machines
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context-Aware RS:
Pre-filtering
+ Simple
+ Works with large amounts of data
- Increases sparseness
- Does not scale well with many Context variables
Post-filtering
+ Single model
+ Takes into account context interactions
- Computationally expensive
- Increases data sparseness
- Does not model the Context directly
Tensor Factorization & Factorization Machines
+ Performance
+ Linear scalability
+ Models context directly
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ranking
Most recommendations are presented in a
sorted list
Recommendation is a ranking problem
Popularity is the obvious baseline
Users pay attention to few items at the top of
the list
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ranking: Approaches
(I) Re-rank: based on features e.g.
predicted rating, popularity, etc
(II) Learning to Rank: Build Ranking CF
models
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank
Machine learning task: Rank the most relevant
items as high as possible in the
recommendation list
Does not try to predict a rating, but the order
of preference
Training data have partial order or binary
judgments (relevant/not relevant)
Can be treated as a standard supervised
classification problem
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Matrix Factorization for Ranking
2
5
4
5
4
4
1
5
5
4
1
2
5
2
4
1
d
d
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Approaches
1) Pointwise
Ranking function minimizes loss function defined
on individual relevance judgment e.g.
Ranking score based on regression or
classification
Ordinal regression, Logistic regression, SVM
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Approaches
2) Pairwise
Loss function is defined on pair-wise
preferences
Goal: minimize number of inversions in
ranking
BPR, RankBoost, RankNet, FRank…
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Metrics
Metrics evaluate the quality of a recommendation list
- Normalized Discounted Cumulative Gain NDCG
Computed for the first k items
The NDCG@k of a list of items ratings Y, permuted by π is:
where πs is the permutation which sorts Y decreasingly
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Metrics
1/log(7)3/log(6)7/log(5)15/log(4)31/log(3)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Metrics
Mean Reciprocal Rank (MRR)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Metrics
Mean Average Precision (MAP)
S the set of relevant items, N #users
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Non-smoothness of Metrics
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Non-smoothness of Metrics
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Non-smoothness of Metrics
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Approaches
3) List-wise
Direct optimization of ranking metrics,
List-wise loss minimization for CF a.k.a
Collaborative Ranking
CoFiRank: optimizes an upper bound of
NDCG (Smooth version)
CLiMF : optimizes a smooth version of MRR
TFMAP: optimizes a smooth version of MAP
AdaRank: uses boosting to optimize NDCG
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Overview
Point-wise Pair-wise List-wise
Matrix factorization
[Koren 2009]
BPR [Rendle 2009] CofiRank [Weimer 2007]
SVD++ [Koren 2008] EigenRank [Liu 2008] ListRank [Shi 2010]
OrdRec [Koren 2011] pLPA [Liu 2009] WLT [Volkovs 2012]
Factorization machines
[Rendle 2012]
CR [Balakrishnan 2012] TFMAP [Shi 2012a]
(All rating prediction
methods)
CLiMF [Shi 2012b]
GAPfm [Shi 2013a]
xCLiMF [Shi 2013b]
Classification by What to Model
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Approaches
3) List-wise
Direct optimization of ranking metrics,
List-wise loss minimization for CF a.k.a
Collaborative Ranking
CoFiRank: optimizes an upper bound of
NDCG (Smooth version)
CLiMF : optimizes a smooth version of MRR
TFMAP: optimizes a smooth version of MAP
AdaRank: uses boosting to optimize NDCG
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to rank - Overview
Proxy of Rankings Structured
Estimation
Non-smooth
optimization
Smoothing
ranking
measures
( Rating predicting
methods
CofiRank
[Weimer 2007]
WLT
[Volkovs 2012]
BPR
[Rendle 2009]
EigenRank
[Liu 2008]
TFMAP
[Shi 2012a]
pLPA
[Liu 2009]
CLiMF
[Shi 2012b]
ListRank
[Shi 2010]
GAPfm
[Shi 2013a]
CR
[Balakrishnan 2012]
XCLiMF
[Shi 2013b]
Classification by How to Model
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ordrec [Koren 2011]
Pointwise, proxy
11 22 33 44 55
t1
t2
t3
t4
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ordrec [Koren 2011]
Pointwise, proxy
11 22 33 44 55
t1
t2
t3
t4
Parameterize the threshold into:
User factor,
item factor and
previous threshold
Full distribution estimation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
BPR [Rendel 2009]
Pair-wise, smoothing
Main idea: learn by comparing items that the user likes with items that he does not.
When dealing with implicit data sample the unseen data as negative
Optimize the AUC, use a smooth version for Optimization purposes
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
BPR [Rendel 2009]
Pair-wise, smoothing
Representation of pairwise preference data
What Alice watched What Alice’s pairwise preferences
(Row to Column)
Transformers (T) ?
Inception (I) ?
Scent of a woman (S) 1
Forrest Gump (F) 1
Zombieland (Z) ?
T I S F Z
T ? - - ?
I ? - - ?
S + + ? +
F + + ? +
Z ? ? - -
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ordrec [Koren 2011]
Ratings are not numeric, but ordinal.
Ordinal modeling by learning the thresholds
Items rated similarly are similar with each other.
An item rated 4 should be
more similar to an item rated 5
rather than to an item rated 1
Rating
1
2
3
4
5
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
i1 i2 i3
u1 2 3 4
u2 4 2 5
EigenRank [Liu 2008]
Pair-wise, proxy
Ranking-based similarity instead of rating based similarity
User-user similarity based on
Kendall Rank Correlation
Neighbour selection
for a user & item
preference estimation
Random walk with restart
over pair-wise
item preference graph
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
List-wise
Learn to model individual ratings
CofiRank
ListRank
CLiMF
TFMAP
GAPfm
xCLiMF
Learn to model ranked list, or to
model evaluation metrics that
capture the quality of ranked list
List-wise
perspective
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CoFiRank [Weimer 2007]
list-wise, structured est.
Based on Structured estimation i.e. Machine
Learning methods for complex output domains
(graphs, sequences, etc.)
Cast the Ranking problem as a Structured
estimation problem
i.e. Learn a function that is maximized for the best
possible ranking with respect to a ranking
measure e.g. nDCG
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CoFiRank [Weimer 2007]
list-wise, structured est.
Step 1:
Instead of maximizing
Minimize
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CoFiRank [Weimer 2007]
list-wise, structured est.
Step 2:Step 2:
Use
Create a linear mapping
That is maximized for
Denote by a decreasing non-negative sequence
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CoFiRank [Weimer 2007]
list-wise, structured est.
Step 3:Step 3:
Find convex upper on non-convex optimization problems:
Let
Maximization by solving a Linear Assignment Problem to get
an estimate for the worst case loss (maximum margin)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CoFiRank [Weimer 2007]
list-wise, structured est.
[3, 2 ,4 ,1] [4, 2 ,1 ,3]
[1, 0.7 ,0.57 ,0.5]
[-0.5, 0.0 ,0.42 ,0.1]
2.9 2.6 1.9 1.6
1.6 2.6 2.9 1.9
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CoFiRank [Weimer 2007]
list-wise, structured est.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CLiMF [Shi 2012b]
List-wise, Smoothing Ranking Metrics
Why optimize mean reciprocal rank (MRR)?
Focus at the very top of the list
Try to get at least one interesting item at the top of
the list
How to optimize MRR?
Find a smooth version of MRR
Find a lower bound of the smoother MRR
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Graphical Notation RelevantRelevant
Irrelevant
Irrelevant
Irrelevant
Irrelevant
Irrelevant
RelevantRelevant
RelevantRelevant
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ranking using latent
representation
55
44
11
44
22
11
22
11
[0.180, 0.019][0.180, 0.019]
[0.013, 0.487][0.013, 0.487]
[0.165, 0.632]
[0.730, 0.109][0.730, 0.109]
[0.420, 0.485]
[0.463, 0.725]
[0.894, 0.857]
[0.942, 0.851]
If user = [-100, -100]
• 2d latent factor
• We get the corresponding ranking
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
2
1
1
44
44
1
55
2
[0.894, 0.857]
[0.463, 0.725]
[0.942, 0.851]
[0.730, 0.109][0.730, 0.109]
[0.013, 0.487][0.013, 0.487]
[0.165, 0.632]
[0.180, 0.019][0.180, 0.019]
[0.420, 0.485]
Matrix Factorization
(for ranking)
• Randomly initialize item vectors
• Randomly initialize user vectors
• While not converged
• Compute rating prediction error
• Update user factors
• Update item factors
• Let's say user is [-100, -100]
• Compute the square error
(5-<[-100, -100], [0.180, 0.19]>)2=1764
• Update user & item factors
to the direction where the error is reduced
(according to the gradient of the loss)
8 items with ratings
& random factors
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning: Stochastic Gradient
Descent with Square Loss
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Square Loss
User: [3, 1], RMSE=6.7
1
2
44
1
2
1
55
44
[0.942, 0.851]
[0.894, 0.857]
[0.730, 0.109][0.730, 0.109]
[0.463, 0.725]
[0.420, 0.485]
[0.165, 0.632]
[0.180, 0.019][0.180, 0.019]
[0.013, 0.487][0.013, 0.487]
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Example:
average precision (AP)
• AP: we compute the precision at each relevant position
and average them
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Why is hard? Non Smoothness
Example: AP
u:[-20,-20]
u:[20,20]
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
AP vs. RMSE
The minimum in RMS and maximum in MAP do not correspond!
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Non-smoothness of AP
ymi
=1 if item i is relevant for user m; else ymi
= 0
The indicator function = 1 if it is true; else =0
rmi
= rank of item i for user m
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
AP vs. RMSE
The minimum in RMS and maximum in MAP do not correspond!
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to Rank for Top-k RecSys
•Usually we care to make accurate ranking (and not rating) prediction
•Square loss optimizes to accurately predict 1s and 5s.
•RS should get the top k items right -> Ranking problem
•Why not to learn how to rank directly?
•Learning to Rank methods provide up to 30% performance
improvements in off-line evaluations
•It is possible, but a more complex task
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
How can we get a smooth AP?
• We replace non smooth places of MAP with smooth approximation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
How can we get a smooth MAP?
• We replace non smooth places of MAP with smooth approximation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Smooth version of MAP
u:[-20,-20]
u:[20,20]
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Not always the case:
only sometimes approximation
is very good…
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ranking Inconsistencies
Achieving a perfect ranking for all users is not possible
Two Sources of Inconsistencies:
●
Factor Models (all models) have limited expressive power
and cannot learn the perfect ranking for all users
Ranking functions approximations are inconsistent e.g.
A > B & B > C but C > A
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Summary on Ranking 101
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Ranking Metrics:
AUC
RR
AP
nDCG
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
AUC: Area Under the ROC Curve
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
RR: Reciprocal Rank
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
AP: Average Precision
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
AP vs.RR
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
nDCG: Normalized Discounted
Cumulative Gain
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
AP AUC
RR Square Loss
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Top-k Ranking
Focus on the very top of the recommendation list
since users pay attention only to the first k items
Top heavy ranking measures put much more
emphasis at the top of the list (e.g. MRR, MAP,
NDCG).
Drop in measure non-linear with the position in
the list
AUC gives the same emphasis to the top as to
the bottom of the list.
Drop in measure linear to the position in the list
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to Rank for RecSys
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to Rank for IR & ML
Query-document (Q-D) retrieval:
Given a query, rank documents.
RankSVM
LambdaRank
RankNet
RankBoostAdaR
ank
ListNet
SVM-MAP
SoftRank
...
Features of a Q-D pair,
relevance label
Learn a ranking function
Predict relevances of new Q-D pairs
Analogy: Query-doc --- User-item
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Learning to Rank for RecSys
Focus on the very top of the recommendation list
since users pay attention only to the first k items
Top heavy ranking measures put much more
emphasis at the top of the list (e.g. MRR, MAP,
NDCG).
Drop in measure non-linear with the position in
the list
AUC gives the same emphasis to the top as to
the bottom of the list.
Drop in measure linear to the position in the list
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
The non-smoothness of
Reciprocal Rank
Reciprocal Rank (RR): The inverse of the rank of
the first relevant item in a given list.
Rank-based component Rank-based component
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Reciprocal Rank
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Lower Bound
Jensen’s inequality Concavity of log function
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
What's the key?
CLiMF reciprocal rank loss essentially pushes
relevant items apart
In the process at least one items ends up
high-up in the list
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Conventional Loss
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
CLiMF MRR-Loss
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Optimizing RR vs. AUC
Opt. RR Opt. AUC
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
List-wise
xCLiMF [Shi 2013b] – Generalization to Ratings
Expected reciprocal rank (ERR) [Chapelle 2009]
A generalization of RR to graded relevance data
Cascade nature compared to NDCG
Equal contribution
independent of the
items ranked above
Different contributions
relative to the items
ranked above
NDCG ERR
0
1
0
3
0
0
5
0
3
0
0
1
0
3
0
0
5
0
3
0
= <
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
xCLiMF vs. CLiMF
Results from Tuenti video watching dataset
xCLiMF shows its advantage over CLiMF
when applied to graded relevance data
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
List-wise
TFMAP [Shi 2012a] – Generalization to Context
Context-aware Recommendation
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Problem Setting
Given: Users’ implicit feedback on items under different contexts
Target: To recommend a list of items to each user under any given
context, as accurate as possible
Top-N recommendation
Context-aware
Optimal in terms of MAP
Items
Users
Contexts
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
TFMAP: Main concept
CP tensor factorization
U, V, C are latent factors
(parameters to be
learned)
U, V, C are learned from
optimizing the smoothed
MAP
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Social and Trust-based
recommenders
A social RS recommends items that are “popular”
with the friends of the user.
Friendship though does not imply trust
“trust” in social-based RS can be per-user or
topic-specific
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Trust Inference
Select 2 users: source (node A) & sink (C)
Recommend to the source how much to trust the sink.
source sink
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Major Algorithms - Networks
Advogato (Levien)
Appleseed (Ziegler and Lausen)
MoleTrust (Massa and Avesani)
TidalTrust (Golbeck)
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Building Recommender Systems
Using Trust
Use trust as a way to give more weight to
some users
Trust for collaborative filtering
Use trust in place of (or combined with)
similarity
Trust for sorting and filtering
Prioritize information from trusted sources
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Social Recommendations
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Other ways to use Social
Social connections can be used in
combination with other approaches
In particular, “friendships” can be fed into
collaborative filtering methods in different
ways
e.g. replace or modify user-user “similarity” by
using social network information
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Hybridization Methods
Hybridization Method Description
Weighted Outputs (scores or votes) from several
techniques are combined with different
degrees of importance to offer final
recommendations
Switching Depending on situation, the system changes
from one technique to another
Mixed Recommendations from several techniques
are presented at the same time
Feature combination Features from different recommendation
sources are combined as input to a single
technique
Cascade The output from one technique is used as
input of another that refines the result
Feature augmentation The output from one technique is used as
input features to another
Meta-level The model learned by one recommender is
used as input to another
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Weighted
Rating for an item is computed as the weighted sum of
ratings produced by a pool of different RS.
The weights are determined by training and get adjusted as
new ratings arrive.
Assumption: relative performance of the different techniques
is uniform. Not true in general: e.g. CF performs worse for
items with few ratings.
e.g.
P-Tango RS: a CB and a CF recommender equally
weighted at first. Weights are adjusted as predictions are
confirmed or not.
RS with consensus scheme: each recommendation of a
specific item counts as a vote for the item.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Feature Combination
CF ratings of users are passed as additional feature to a CB.
CB makes recommendations over this augmented data set.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Switching
The system uses a criterion to switch between
techniques
The main problem is to identify a good switching
criterion.
e.g.
The DailyLearner system uses a CB-CF. When CB cannot
predict with sufficient confidence, it switches to CF.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Mixed
Recommendations from more than one technique are
presented together
e.g.
The PTV system recommends a TV viewing schedule for
the user by combining recommendations from a CB and
a CF system.
CB uses the textual descriptions of TV shows; vs CF uses
other users’ preferences.
When collision occurs, the CB has priority.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Cascade
At each iteration, a first recommendation technique
produces a coarse ranking & a second technique refines the
recommendation
Cascading avoids employing the second, lower-priority,
technique on items already well-differentiated by the first
Requires a meaningful ordering of the techniques.
E.g.: EntreeC is a restaurant RS uses its knowledge of
restaurants to make recommendations based on the user’s
stated interests. The recommendations are placed in buckets
of equal preference, and the collaborative technique breaks
ties
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Feature Augmentation
Very similar to the feature combination method:
Here the output of one RS is incorporated into the
processing of a second RS
e.g.:
Amazon.com generates text data (“related
authors” and “related titles”) using its internal
collaborative systems
Libra system makes content-based
recommendations of books based on these text
data found in Amazon.com, using a naive Bayes
text classifier
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Beyond Explicit Ratings
Implicit feedback is more readily available, and less noisy
Already many approaches (e.g. SVD++) can make use
of implicit feedback
Ongoing research in combining explicit and implicit
feedback
D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian
recommendations. In Proc.of the 18th WWW, 2009.
Koren Y and J. Sill. OrdRec: an ordinal model for predicting personalized item rating
distributions. In Rec-Sys ’11, pages 117–124, 2011.
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative
filtering model. In Proceedings of the 14th ACM SIGKDD, 2008.
Yifan Hu, Y. Koren, and C. Volinsky. Collaborative Filtering for Implicit Feedback
Datasets. In Proc. Of the 2008 Eighth ICDM, pages 263–272, 2008.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Personalized Learning to Rank
Better approaches to learning to rank that
directly optimize ranking metrics and allow
for personalization (e.g. CliMF & TFMAP)
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A.
Hanjalic. CLiMF: learning to maximize reciprocal rank with
collaborative less-is-more filtering. In Proc. of the sixth Recsys,
2012.
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson,A. Hanjalic, and N.
Oliver. TFMAP: optimizing MAP for top-n context-aware
recommendation. In Proc. Of the 35th SIGIR, 2012.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Context-aware
Recommendations
Beyond the traditional 2D user-item space
Recommendations should also respond to user
context (e.g. location, time of the day...)
Many different approaches such as Tensor
Factorization or Factorization Machines
A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse
recommendation: n-dimensional tensor factorization for context-aware
collaborative filtering. In Proc. of the fourth ACM Recsys, 2010.
S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-
aware recommendations with factorization machines. In Proc. of the 34th
ACM SIGIR, 2011.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
User choice and presentation
effects
We log the recommended items to the
users and their choice
We can use this information as negative
feedback (not chosen) and positive
feedback (chosen).
S.H. Yang, B. Long, A.J. Smola, H. Zha, and Z. Zheng.
Collaborative competitive filtering: learning recommender
using context of user choice. In Proc. of the 34th ACM SIGIR,
2011.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Social Recommendations
Beyond trust-based
Cold-starting with Social Information
Combining Social with CF
Finding “experts”
J. Delporte, A. Karatzoglou, T. Matuszczyk, S. Canu. Socially Enabled Preference Learning
from Implicit Feedback Data. In Proc. of ECML/PKDD 2013
N. N. Liu, X. Meng, C. Liu, and Q. Yang. Wisdom of the better few: cold start
recommendation via representative based rating elicitation. In Proc. of RecSys’11, 2011.
M. Jamali and M. Ester. Trustwalker: a random walk model for combining trust-based and
item-based recommendation. In Proc. of KDD ’09, 2009.
J. Noel, S. Sanner, K. Tran, P. Christen, L. Xie, E. V. Bonilla, E. Abbasnejad, and N. Della
Penna. New objective functions for social collaborative filtering. In Proc. of WWW ’12,
pages 859–868, 2012.
X. Yang, H. Steck, Y. Guo, and Y. Liu. On top-k recommendation using social networks. In
Proc. of RecSys’12, 2012.
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Diversity in Recommendation (I)
Recommendations from a music on-line retailer:
No diversity: pop albums from female singers.
Some are redundant.
Born This Way Pink Friday Dangerously in
Love
Born This Way
– The Remix
Femme Fatale Can't be
Tamed
Teenage
Dream
Lady Gaga Nicki Minaj Beyoncé Lady Gaga Britney Spears Miley Cyrus Katy Perry
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Diversity in Recommendation (II)
Some good music recommendations:
Different authors and genres.
Not similar between them.
These are much better recommendations!
Wrecking Ball Not your Kind
of People
Like a Prayer Choice of
Weapon
Sweet Heart
Sweet Light
The Light the
Dead See
Little Broken
Hearts
B. Springsteen Garbage Madonna The Cult Spiritualized Soulsavers Norah Jones
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Re-Ranking
Recommender Re-ranking
top 5
not diverse
D
top 5
diverse
Ziegler et al. 2005
Zhang et al. 2008
Vargas et al. 2011
comedy
drama
action
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Sub-Profiles
comedy
action
drama
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Novelty and Diversity Metrics
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Conclusions
Recommender Systems (RS) are an important
application of Machine Learning
RS have the potential to become as important
as Search is now
However, RS are more than Machine Learning
HCI
Economical models
...
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Conclusions
RS are fairly new but already grounded on
well-proven technology
Collaborative Filtering
Machine Learning
Content Analysis
Social Network Analysis
…
However, there are still many open questions
and a lot of interesting research to do!
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Index
1. Introduction: What is a Recommender System?
2. Approaches
1. Collaborative Filtering
2. Content-based Recommendations
3. Context-aware Recommendations
4. Other Approaches
5. Hybrid Recommender Systems
3. Research Directions
4. Conclusions
5. References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
"Recommender Systems Handbook." Ricci, Francesco, Lior Rokach, Bracha
Shapira, and Paul B. Kantor. (2010).
“Recommender systems: an introduction”. Jannach, Dietmar, et al.
Cambridge University Press, 2010.
“Toward the Next Generation of Recommender Systems: A Survey of the
State-of-the-Art and Possible Extensions”. G. Adomavicious and A. Tuzhilin.
2005. IEEE Transactions on Knowledge and Data Engineering, 17 (6)
“Item-based Collaborative Filtering Recommendation Algorithms”, B.
Sarwar et al. 2001. Proceedings of World Wide Web Conference.
“Lessons from the Netflix Prize Challenge.”. R. M. Bell and Y. Koren. SIGKDD
Explor. Newsl., 9(2):75–79, December 2007.
“Beyond algorithms: An HCI perspective on recommender systems”.K.
Swearingen and R. Sinha. In ACM SIGIR 2001 Workshop on Recommender
Systems
“Recommender Systems in E-Commerce”. J. Ben Schafer et al. ACM
Conference on Electronic Commerce. 1999-
“Introduction to Data Mining”, P. Tan et al. Addison Wesley. 2005
References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
“Evaluating collaborative filtering recommender systems”. J. L.
Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. ACM Trans.
Inf. Syst., 22(1):5–53, 2004.
“Trust in recommender systems”. J. O’Donovan and B. Smyth. In
Proc. of IUI ’05, 2005.
“Content-based recommendation systems”. M. Pazzani and D.
Billsus. In The Adaptive Web, volume 4321. 2007.
“Fast context-aware recommendations with factorization
machines”. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-
Thieme. In Proc. of the 34th ACM SIGIR, 2011.
“Restricted Boltzmann machines for collaborative filtering”.R.
Salakhutdinov, A. Mnih, and G. E. Hinton.In Proc of ICML ’07, 2007
“Learning to rank: From pairwise approach to listwise approach”.Z.
Cao and T. Liu. In In Proceedings of the 24th ICML, 2007.
“Introduction to Data Mining”, P. Tan et al. Addison Wesley. 2005
References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Balakrishnan, S., & Chopra, S. (2012, February). Collaborative ranking. In Proceedings of the fifth ACM international
conference on Web search and data mining (pp. 143-152). ACM.
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005, August). Learning to rank
using gradient descent. In Proceedings of the 22nd international conference on Machine learning (pp. 89-96). ACM.
Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., & Li, H. (2007, June). Learning to rank: from pairwise approach to listwise approach.
In Proceedings of the 24th international conference on Machine learning (pp. 129-136). ACM.
Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009, November). Expected reciprocal rank for graded relevance. In
Proceedings of the 18th ACM conference on Information and knowledge management (pp. 621-630). ACM.
Joachims, T. (2002, July). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD
international conference on Knowledge discovery and data mining (pp. 133-142). ACM.
Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434).
ACM.
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
Koren, Y., & Sill, J. (2011, October). OrdRec: an ordinal model for predicting personalized item rating distributions. In
Proceedings of the fifth ACM conference on Recommender systems (pp. 117-124). ACM.
Liu, N. N., & Yang, Q. (2008, July). Eigenrank: a ranking-oriented approach to collaborative filtering. In Proceedings of the
31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 83-90). ACM
Liu, N. N., Zhao, M., & Yang, Q. (2009, November). Probabilistic latent preference analysis for collaborative filtering. In
Proceedings of the 18th ACM conference on Information and knowledge management (pp. 759-766). ACM.
Quoc, C., & Le, V. (2007). Learning to rank with nonsmooth cost functions. Proceedings of the Advances in Neural
Information Processing Systems, 19, 193-200.
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009, June). BPR: Bayesian personalized ranking from
implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp. 452-461).
AUAI Press.
References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007, June). Restricted Boltzmann machines for collaborative filtering. In
Proceedings of the 24th international conference on Machine learning (pp. 791-798). ACM.
Shi, Y., Larson, M., & Hanjalic, A. (2010, September). List-wise learning to rank with matrix factorization for collaborative
filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 269-272). ACM.
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., & Oliver, N. (2012a, August). TFMAP: Optimizing MAP for top-
n context-aware recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and
development in information retrieval (pp. 155-164). ACM.
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., & Hanjalic, A. (2012b, September). CLiMF: learning to maximize
reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender
systems (pp. 139-146). ACM.
Taylor, M., Guiver, J., Robertson, S., & Minka, T. (2008, February). Softrank: optimizing non-smooth rank metrics. In
Proceedings of the international conference on Web search and web data mining (pp. 77-86). ACM..
Volkovs, M., & Zemel, R. S. (2012). Collaborative ranking with 17 parameters. In Advances in Neural Information Processing
Systems (pp. 2303-2311).
Weimer, M., Karatzoglou, A., Le, Q. V., & Smola, A. J. (2007). Cofi rank-maximum margin matrix factorization for
collaborative ranking. In Advances in neural information processing systems (pp. 1593-1600).
Xu, J., & Li, H. (2007, July). Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual
international ACM SIGIR conference on Research and development in information retrieval (pp. 391-398). ACM.
Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007, July). A support vector method for optimizing average precision. In
Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
(pp. 271-278). ACM.
References
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Recsys Wiki: http://recsyswiki.com/
Recsys conference Webpage: http://recsys.acm.org/
Recommender Systems Books Webpage:
http://www.recommenderbook.net/
Mahout Project: http://mahout.apache.org/
MyMediaLite Project: http://www.mymedialite.net/
Online resources
MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems
Thank you!
Questions?
Alexandros Karatzoglou
alexk@tid.es
@alexk_z

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Machine Learning for Recommender Systems MLSS 2015 Sydney

  • 1. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems MLSS 2015 Sydney Machine Learning for Recommender Systems Alexandros Karatzoglou Senior Research Scientist @ Telefonica Research, Barcelona alexk@tid.es @alexk_z
  • 2. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Telefonica Research Machine Learning Recommender Systems HCI Mobile Computing Systems Networks http://www.tid.es
  • 3. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Tuenti
  • 4. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Mozilla FirefoxOS Marketplace
  • 5. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Recent Publications SIGIR 2014: Gaussian Process Factorization Machines for Context-aware Recommendation RecSys 2014: Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems RecSys 2014: Question Recommendation for Collaborative Question Answering Systems with RankSLDA CIKM 2013: GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains RecSys 2013: xCLiMF: Optimizing Expected Reciprocal Rank for Data with Multiple Levels of Relevance ECML/PKDD 2013: Socially Enabled Preference Learning from Implicit Feedback Data CIKM 2012: Climbing the App Wall: Enabling Mobile App Discovery through Context-Aware Recommendations RecSys 2012: CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering * Best Paper Award SIGIR 2012: TFMAP: Optimizing MAP for Top-N Context-aware Recommendation RecSys 2011: Collaborative Temporal Order Modeling RecSys 2011: Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping RecSys 2010: Multiverse Recommendation: N-dimensional Tensor Factorization for Context-Aware Collaborative Filtering EC-Web 2010: Quantile Matrix Factorization for Collaborative Filtering AISTATS 2010: Collaborative Filtering on a Budget RecSys 2009: Maximum Margin Code Recommendation RecSys 2008: Adaptive Collaborative Filtering Machine Learning Journal, 2008: Improving Maximum Margin Matrix Factorization * Best Machine Learning Paper Award at ECML PKDD 2008 NIPS 2007: CoFiRank - Maximum Margin Matrix Factorization for Collaborative Ranking
  • 6. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1.Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Ranking 5. Diversity 3. Research Directions 4. Conclusions 5. References
  • 7. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems From Search to Recommendation “The Web is leaving the era of search and entering one of discovery. What's the difference? SearchSearch is what you do when you're looking for something. DiscoveryDiscovery is when something wonderful that you didn't know existed, or didn't know how to ask for, finds you.” – CNN Money, “The race to create a 'smart' Google
  • 8. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems The value of recommendations Netflix: 2/3 of the movies watched are recommended Google News: recommendations generate 38% more click-throughs Amazon: 35% sales from recommendations Choicestream: 28% of the people would buy more music if they found what they liked.
  • 9. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems The “Recommender problem” Estimate a utility functionutility function to predictpredict how a user will likelike an item.
  • 10. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems The “Recommender problem” C:= {users} S:= {recommendable items} u:= utility function, measures the usefulness of item s to user c, u : C X S→ R where R:= {recommended items}. For each user c, we want to choose the items s that maximize u. ∀c∈C , s'c=argmax (u(c , s)) s∈S
  • 11. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems A good recommendation is relevant to the user: personalized
  • 12. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems A good recommendation is diverse: it represents all the possible interests of one user
  • 13. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems A good recommendation Does not recommend items the user already knows or would have found anyway. Expands the user's taste into neighboring areas. SerendipitySerendipity = Unsought finding= Unsought finding
  • 14. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Top k recommendations Usually users take into account only few suggestions. There is a need to do better on the top scoring recommended items
  • 15. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Ranking 5. Diversity 3. Research Directions 4. Conclusions 5. References
  • 16. Alexandros Karatzoglou – September 06, 2013 – Recommender Systems Approaches to Recommendation Recommendations are based on: Users' past behaviour (Collaborative Filtering – CF): Find similar users to me and recommend what they liked (Memory-based) Model taste patterns in large amounts of user-item interactions (Model-based). Item features (Content-based) Users' features (Demographic) Social recommendations (Trust-based)
  • 17. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems What works? Depends on the domain and particular problem Currently, the best approach is Collaborative Filtering. Other approaches can be combined to improve results What matters? Data preprocessing: outlier removal, denoising, removal of global effects Combining methods
  • 18. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2.1. Collaborative Filtering
  • 19. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 20. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Collaborative Filtering The task of predictingpredicting (filtering) user preferences on new items by collectingcollecting taste information from many users (collaborative). Challenges: many items to choose from very few recommendations to propose few data per user no data for new user very large datasets
  • 21. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: User-based CF Target (or Active) user for whom the CF recommendation task is performed
  • 22. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 2 4 1 Example Each user has expressed an opinion for some items: Explicit opinion: rating score Implicit: purchase records or listen to tracks
  • 23. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: User-based CF 1. Identify set of items rated by the target user
  • 24. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: User-based CF 1. Identify set of items rated by the target user 2. Identify which other users rated 1+ items in this set (neighbourhood formation)
  • 25. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 3. Compute how similar each neighbour is to the target user (similarity function) 4. In case, select k most similar neighbours User-based Similarity
  • 26. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 5. Predict ratings for the target user's unrated items (prediction function) 6. Recommend to the target user the top N products based on the predicted ratings User-based CF
  • 27. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems User-based CF Target user u, ratingsratings matrix Y yv,i → rating by user v for item i Similarity Pearson r correlation sim(u,v) between users u & v Predicted rating
  • 28. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 sim(u,v) NA NA Example: User-based CF
  • 29. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: User-based CF 0.87 sim(u,v) NA NA
  • 30. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 1 sim(u,v) 0.87 NA NA Example: User-based CF
  • 31. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 -1 0.87 1 sim(u,v) NA NA Example: User-based CF
  • 32. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 3.51* 4 5 4 4 1 3.81* 5 5 4 2.42* 1 2 5 2.48* 4 1 2 -1 0.87 1 sim(u,v) NA NA Example: User-based CF
  • 33. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: Item-based CF Target item: item for which the CF prediction task is performed.
  • 34. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Item-based CF The basic steps: Identify set of users who rated the target item i Identify which other items (neighbours) were rated by the users set Compute similarity between each neighbour & target item (similarity function) In case, select k most similar neighbours Predict ratings for the target item (prediction function)
  • 35. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Item Based Similarity
  • 36. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Item Based Similarity Target item I yu,j → rating of user u for item j, average rating for j. Similarity sim(i,j) between items i and j (Pearson- correlation) Predicted rating
  • 37. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: Item-based CF -1sim(i,j)
  • 38. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: Item-based CF -1-1sim(i,j)
  • 39. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: Item-based CF 0.86-1-1sim(i,j)
  • 40. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: Item-based CF 1-1-1 0.86sim(i,j)
  • 41. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 1 2 Example: Item-based CF sim(6,5) cannot be calculated 1-1-1 0.86sim(i,j) NA
  • 42. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 5 4 5 4 4 1 5 5 4 1 2 5 4 2.48* 2.94* 1 2 1.12* Example: Item-based CF 1-1-1 0.86sim(i,j) NA
  • 43. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems PearsonPearson r correlation-based Similarityr correlation-based Similarity does not account for user rating biases Cosine-basedCosine-based SimilaritySimilarity does not account for user rating biases AdjustedAdjusted Cosine SimilarityCosine Similarity takes care of user rating biases as each pair in the co-rated set corresponds to a different user. Item Similarity Computation
  • 44. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Performance Implications Bottleneck: Similarity computation. Time complexity, highly time consuming with millions of users & items in the database. Two-step process: “off-line component” / “model”: similarity computation, precomputed & stored. “on-line component”: prediction process.
  • 45. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Two-step process OfflineOffline OnlineOnline Learning Process Model Or Clusters Decision Process Context Recommended Items
  • 46. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Performance Implications User-based similarity is more dynamic. Precomputing user neighbourhood can lead to poor predictions. Item-based similarity is static. We can precompute item neighbourhood. Online computation of the predicted ratings.
  • 47. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Memory based CF + Requires minimal knowledge engineering efforts + Users and products are symbols without any internal structure or characteristics + Produces good-enough results in most cases - Requires a large number of explicit and reliable “ratings” - Requires standardized products: users should have bought exactly the same product - Assumes that prior behaviour determines current behaviour without taking into account “contextual” knowledge
  • 48. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems The Sparsity Problem Typically large product sets & few user ratings e.g. Amazon: in a catalogue of 1 million books, the probability that two users who bought 100 books each, have a book in common is 0.01 in a catalogue of 10 million books, the probability that two users who bought 50 books each, have a book in common is 0.0002 CF must have a number of users ~ 10% of the product catalogue size
  • 49. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems The Sparsity Problem Methods for dimensionality reduction Matrix Factorization Clustering Projection (Principal Component Analysis ...)
  • 50. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Personalised vs Non-Personalised CF CF recommendations are personalized: the prediction is based on the ratings expressed by similar users; neighbours are different for each target user A non-personalized collaborative-based recommendation can be generated by averaging the recommendations of ALL users How would the two approaches compare?
  • 51. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Personalised vs Non-Personalised CF 0,1510,2230,0222811718164974424EachMovie 0,1790,2330,041100020939526040MovieLens 0,1520,2200,725351944910048483Jester MAE Pers MAE Non Pers density total ratings itemsusersData Set Not much difference indeed! vij is the rating of user i for product j and vj is the average rating for product j MAENP = ∑i, j ∣vij−v j∣ num.ratings Mean Average Error Non Personalized:
  • 52. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Model-Based Collaborative Filtering
  • 53. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Model Based CF Algorithms Models are learned from the underlying data rather than heuristics. Develop a model of user ratings: Bayesian network (probabilistic) Clustering (classification) Association rules Other models: linear regression, probabilistic latent semantic analysis ...
  • 54. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Matrix Factorization 2 5 4 5 4 4 1 5 5 4 1 2 5 2 4 1 d d
  • 55. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning: Stochastic Gradient Descent
  • 56. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning: Stochastic Gradient Descent For each rating of item j by user i:
  • 57. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Matrix Factorization 2 5 4 5 4 4 1 5 5 4 1 2 5 2 4 1 d d
  • 58. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Objective Function
  • 59. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Loss Functions for MF Squared error loss: Mean Average Error: Binary Hinge loss:
  • 60. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Restricted Boltzmann Machines A (generative stochastic) Neural Network Learns a probability distribution over its inputs Used in dimensionality reduction, CF, topic modeling, feature learning Essential components of Deep Learning methods (DBN's, DBM's)
  • 61. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Restricted Boltzmann Machines
  • 62. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Restricted Boltzmann Machines Each unit is in a state which can be active or not active. Each input of a unit is associated to a weight The transfer function Σ calculates for each unit a score based on the weighted sum of the inputs This score is passed to the activation function φ which calculated the probability that the unit state is active.
  • 63. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to Rank - Usually we care to make accurate ranking and not rating prediction - Square loss optimizes to accurately predict 1s and 5s. - RS should get the top items right -> Ranking problem - Why not to learn how to rank directly - Learning to Rank methods provide up to 30% performance improvements in off-line evaluation
  • 64. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Restricted Boltzmann Machines Each unit in the visible layer vi corresponds to one item The number of the hidden units hj is a parameter. Each vi is connected to each hj through a weight wij In the trainingtraining phase, for each user: if the user purchased the item the corresponding vi is activated. The activation states of all vi are the input of each hj Based on this input the activation state of each hj is calculated The activation state of all hj become now the input of each vi The activation state of each vi is recalculated For each vi the difference between the present activation state and the previous is used to update the weights wij and thresholds θj
  • 65. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Restricted Boltzmann Machines
  • 66. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Restricted Boltzmann Machines In the predictionprediction phase, using a trained RBM, when recommending to a user: For the items of the user the corresponding vi is activated. The activation states of all vi are the input of each hj Based on this input the activation state of each hj is calculated The activation state of all hj become now the input of each vi The activation state of each vi is recalculated The activation probabilities are used to recommend items
  • 67. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Recurrent Neural Networks for CF RNN's are Neural Networks designed to model sequences RNN for CF tries to predict the next item given all previous ones e.g. predict the next song, video, given the history of streams. RNN's model the current output as a function of the previous output and a hidden (latent) state RNN models the items
  • 68. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Recurrent Neural Networks for CF h0 y0 h1 y1 h2 y2 ...
  • 69. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Recurrent Neural Networks for CF 2 vectors of dimension k: time steps: 0... t-1. “hidden” internal states: h0 ... ht-1 . Output vectors y At each time step: Predict the output yi given the hidden state hi (we need to model P(yi |hi )) Feed yi back into the next hidden state hi+1 : hi+1 = f(a(hi ) + b(yi )). where f is generally some nonlinear function (e.g. sigmoid or tanh), a, b are usually a simple linear transform.
  • 70. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RNN for CF: example Predict a sequence (vector yi ) of items: Assume P(yi |hi ) is a normal distribution the log-likelihood of the loss is the (negative) L2 loss: -(yt – ht )2 hi+1 = tanh(Uyi + Vhi ) & h0 = 0. yi = W hi Learn the k2 unknown parameters (U, V, and W) that maximize the log-likelihood over all:
  • 71. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RNN for CF: example Maximize the log-likelihood through back-propagation. we need to do two steps through each sequence. First propagation: For i=0... t-1: Calculate all h_i. the total log-likelihood is the sum of all log-probabilities of observing each output:
  • 72. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RNN for CF: example Define the partial derivatives of the log-L with respect to the hidden state: Since each hi only influences later hidden states, is just a function of all future j = i... t-1. We can rewrite as a function of : We can evaluate this backwards from t-1...0.
  • 73. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RNN for CF: example P(yi | hi ) and are specified by our model Derive the gradients for the unknown parameter U: Derive similar gradients for V, W. Optimize using SGD over lots of samples.
  • 74. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RNN for CF Given a sequence of watched movies, predict the next one. Now yi is not a scalar, but one out of many items. We need a probability distribution P(yi |hi ). The Softmax distribution over all possible outputs. This means we have to learn a vector aj for each item. P(yi |hi ) is now proportional to :
  • 75. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RNN for CF We also need something linking back the output to the next hidden state. we will learn another set of vectors bj – one for each item. we don’t need the matrices U and V. Our model now becomes:   Again, we maximize the total log-likelihood Note that we have the unknowns aj ‘s and bj ‘s for each item. [Erik Bernhardsson
  • 76. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Clustering ClusterCluster customers into categories based on preferences & past purchases ComputeCompute recommendations at the cluster level: all customers within a cluster receive the same recommendations
  • 77. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Clustering B, C & D form 1 CLUSTER vs. A & E form another cluster. « Typical » preferences for CLUSTER are: Book 2, very high Book 3, high Books 5 & 6, may be recommended (Books 1 & 4, not recommended)
  • 78. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Clustering Customer F is classified as a new member of CLUSTER will receive recommendations based on the CLUSTER's preferences : Book 2 will be highly recommended to Customer F Book 6 will also be recommended to some extent
  • 79. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Clustering + It can also be applied for selecting the k most relevant neighbours in a CF algorithm + Faster: recommendations are per cluster - less personalized: recommendations are per cluster vs. in CF they are per user
  • 80. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Association rules Past purchases used to find relationships of common purchases
  • 81. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Association rules + Fast to implement + Fast to execute + Not much storage space required + Not « individual » specific + Very successful in broad applications for large populations, such as shelf layout in retail stores - Not suitable if preferences change rapidly - Rules can be used only when enough data validates them. False associations can arise
  • 82. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Limitations of CF ColdCold Start: It needs to have enough users in the system. New items need to get enough ratings. Sparsity: it is hard to find users who rated the same items. Popularity Bias: Cannot recommend items to users with unique tastes. Tends to recommend popular items.
  • 83. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Cold-start New User ProblemNew User Problem: the system must first learn the user’s preferences from the ratings. Hybrid RS, which combines content-based and collaborative techniques, can help. New Item ProblemNew Item Problem: Until the new item is rated by a substantial number of users, the RS is not able to recommend it.
  • 84. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 85. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Content-Based Recommendations Recommendations are based on the information on the contentcontent of itemsof items rather than on other users’ opinions. Use a machine learning algorithm to model the users' preferences from examples based on a description of the content.
  • 86. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems What is content of an item? Explicit attributes or characteristics e.g. for a movie: Genre: Action / adventure Feature: Bruce Willis Year: 1995 Textual content e.g. for a book: title, description, table of content
  • 87. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems In Content-Based Recommendations... The recommended items for a user are based on the profile built up by analysing the content of the items the user has liked in the past
  • 88. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Content-Based Recommendation Suitable for text-based products (web pages, books) Items are “described” by their features (e.g. keywords) Users are described by the keywords in the items they bought Recommendations based on the match between the content (item keywords) and user keywords The user model can also be a classifier (Neural Networks, SVM, Naïve Bayes...)
  • 89. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Advantages of CB Approach + No need for data on other users. + No cold-start or sparsity problems. + Can recommend to users with unique tastes. + Can recommend new and unpopular items + No first-rater problem. + Can provide explanations of recommended items by listing content-features that caused an item to be recommended.
  • 90. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Disadvantages of CB Approach - Only for content that can be encoded as meaningful features. - Some types of items (e.g. movies, music)are not amenable to easy feature extraction methods - Even for texts, IR techniques cannot consider multimedia information, aesthetic qualities, download time: a positive rating could be not related to the presence of certain keywords - Users’ tastes must be represented as a learnable function of these content features. - Hard to exploit quality judgements of other users. - Difficult to implement serendipity
  • 91. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Content-based Methods Content(s):= item profile, i.e. a set of attributes/keywords characterizing item s. weight wij measures the 'Importance” (or “informativeness”) of word kj in document dj term frequency/inverse document frequency(TF-IDF) is a popular weighting technique in IR
  • 92. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Content-based User Profile ContentBasedProfile(c):= profile of user c profiles are obtained by: analysing the content of the previous items using keyword analysis techniques e.g., ContentBasedProfile(c):=(wc1, . . . , wck) a vector of weights, where wci denotes the importance of keyword ki to user c
  • 93. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Similarity Measurements In content-based systems, the utility function u(c,s) is defined as: where ContentBasedProfile(c) of user c and Content(s) of document s are both represented as TF-IDF vectors of keyword weights. uc ,s=scoreContentBasedProfile c ,Content s
  • 94. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Similarity Measurements Utility function u(c,s) usually represented by some scoring heuristic defined in terms of vectors, such as the cosine similarity measure. uc ,s=cos wc , ws= wc⋅w s ∥ wc∥2×∥ ws∥2 = ∑i=1 K wi , c wi , s ∑i=1 K wi , c 2 ∑i=1 K wi , s 2
  • 95. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Content-based Recommendation. An (unrealistic) example How to compute recommendations of books based onlyon their title? A customer buys the book: Building data mining applications for CRM 7 Books are possible candidates for a recommendation: Accelerating Customer Relationships: Using CRM and Relationship Technologies Mastering Data Mining: The Art and Science of Customer Relationship Management Data Mining Your Website Introduction to marketing Consumer behaviour Marketing research, a handbook Customer knowledge management
  • 96. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems building data mining applications for crm Accelerating Customer Relationships: Using CRM and Relationship Technologies Mastering Data Mining: The Art and Science of Customer Relationship Management Data Mining Your Website Introduction to marketing consumer behavior marketing research, a handbook customer knowledge management a 1 accelerating 1 and 1 1 application 1 art 1 behavior 1 building 1 consumer 1 crm 1 1 customer 1 1 1 data 1 1 1 for 1 handbook 1 introduction 1 knowledge 1 management 1 1 marketing 1 1 mastering 1 mining 1 1 1 of 1 relationship 2 1 research 1 science 1 technology 1 the 1 to 1 using 1 website 1 your 1 COUNT
  • 97. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Content-based Recommendation Computes distances between this book & all others Recommends the « closest » books: #1: Data Mining Your Website #2: Accelerating Customer Relationships: Using CRM and Relationship Technologies #3: Mastering Data Mining: The Art and Science of Customer Relationship Management
  • 98. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems building data mining applications for crm Accelerating Customer Relationships: Using CRM and Relationship Technologies Mastering Data Mining: The Art and Science of Customer Relationship Management Data Mining Your Website Introduction to marketing consumer behavior marketing research, a handbook customer knowledge management a 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.000 accelerating 0.000 0.432 0.000 0.000 0.000 0.000 0.000 0.000 and 0.000 0.296 0.256 0.000 0.000 0.000 0.000 0.000 application 0.502 0.000 0.000 0.000 0.000 0.000 0.000 0.000 art 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000 behavior 0.000 0.000 0.000 0.000 0.000 0.707 0.000 0.000 building 0.502 0.000 0.000 0.000 0.000 0.000 0.000 0.000 consumer 0.000 0.000 0.000 0.000 0.000 0.707 0.000 0.000 crm 0.344 0.296 0.000 0.000 0.000 0.000 0.000 0.000 customer 0.000 0.216 0.187 0.000 0.000 0.000 0.000 0.381 data 0.251 0.000 0.187 0.316 0.000 0.000 0.000 0.000 for 0.502 0.000 0.000 0.000 0.000 0.000 0.000 0.000 handbook 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.000 introduction 0.000 0.000 0.000 0.000 0.636 0.000 0.000 0.000 knowledge 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.763 management 0.000 0.000 0.256 0.000 0.000 0.000 0.000 0.522 marketing 0.000 0.000 0.000 0.000 0.436 0.000 0.368 0.000 mastering 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000 mining 0.251 0.000 0.187 0.316 0.000 0.000 0.000 0.000 of 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000 relationship 0.000 0.468 0.256 0.000 0.000 0.000 0.000 0.000 research 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.000 science 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000 technology 0.000 0.432 0.000 0.000 0.000 0.000 0.000 0.000 the 0.000 0.000 0.374 0.000 0.000 0.000 0.000 0.000 to 0.000 0.000 0.000 0.000 0.636 0.000 0.000 0.000 using 0.000 0.432 0.000 0.000 0.000 0.000 0.000 0.000 website 0.000 0.000 0.000 0.632 0.000 0.000 0.000 0.000 your 0.000 0.000 0.000 0.632 0.000 0.000 0.000 0.000 TFIDF Normed Vectors Data 0.187 0.316 Data mining your website Mastering Data Mining: The Art and Science of Customer Relationship Management
  • 99. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 100. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context Context is a dynamic set of factors describing the state of the user at the moment of the user's experience Context factors can rapidly change and affect how the user perceives an item
  • 101. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context in Recommendations Temporal: Time of the day, weekday/end Spatial: Location, Home, Work etc. Social: with Friends, Family Recommendations should be tailored to the user & to the current Context of the user
  • 102. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Level of Adaptation
  • 103. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context-Aware RS: Pre-filtering
  • 104. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context-Aware RS: Post-filtering
  • 105. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context-Aware RS: Tensor Factorization
  • 106. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Factorization Machines
  • 107. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context-Aware RS: Pre-filtering + Simple + Works with large amounts of data - Increases sparseness - Does not scale well with many Context variables Post-filtering + Single model + Takes into account context interactions - Computationally expensive - Increases data sparseness - Does not model the Context directly Tensor Factorization & Factorization Machines + Performance + Linear scalability + Models context directly
  • 108. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 109. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ranking Most recommendations are presented in a sorted list Recommendation is a ranking problem Popularity is the obvious baseline Users pay attention to few items at the top of the list
  • 110. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ranking: Approaches (I) Re-rank: based on features e.g. predicted rating, popularity, etc (II) Learning to Rank: Build Ranking CF models
  • 111. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank Machine learning task: Rank the most relevant items as high as possible in the recommendation list Does not try to predict a rating, but the order of preference Training data have partial order or binary judgments (relevant/not relevant) Can be treated as a standard supervised classification problem
  • 112. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Matrix Factorization for Ranking 2 5 4 5 4 4 1 5 5 4 1 2 5 2 4 1 d d
  • 113. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Approaches 1) Pointwise Ranking function minimizes loss function defined on individual relevance judgment e.g. Ranking score based on regression or classification Ordinal regression, Logistic regression, SVM
  • 114. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Approaches 2) Pairwise Loss function is defined on pair-wise preferences Goal: minimize number of inversions in ranking BPR, RankBoost, RankNet, FRank…
  • 115. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Metrics Metrics evaluate the quality of a recommendation list - Normalized Discounted Cumulative Gain NDCG Computed for the first k items The NDCG@k of a list of items ratings Y, permuted by π is: where πs is the permutation which sorts Y decreasingly
  • 116. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Metrics 1/log(7)3/log(6)7/log(5)15/log(4)31/log(3)
  • 117. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Metrics Mean Reciprocal Rank (MRR)
  • 118. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Metrics Mean Average Precision (MAP) S the set of relevant items, N #users
  • 119. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Non-smoothness of Metrics
  • 120. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Non-smoothness of Metrics
  • 121. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Non-smoothness of Metrics
  • 122. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Approaches 3) List-wise Direct optimization of ranking metrics, List-wise loss minimization for CF a.k.a Collaborative Ranking CoFiRank: optimizes an upper bound of NDCG (Smooth version) CLiMF : optimizes a smooth version of MRR TFMAP: optimizes a smooth version of MAP AdaRank: uses boosting to optimize NDCG
  • 123. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Overview Point-wise Pair-wise List-wise Matrix factorization [Koren 2009] BPR [Rendle 2009] CofiRank [Weimer 2007] SVD++ [Koren 2008] EigenRank [Liu 2008] ListRank [Shi 2010] OrdRec [Koren 2011] pLPA [Liu 2009] WLT [Volkovs 2012] Factorization machines [Rendle 2012] CR [Balakrishnan 2012] TFMAP [Shi 2012a] (All rating prediction methods) CLiMF [Shi 2012b] GAPfm [Shi 2013a] xCLiMF [Shi 2013b] Classification by What to Model
  • 124. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Approaches 3) List-wise Direct optimization of ranking metrics, List-wise loss minimization for CF a.k.a Collaborative Ranking CoFiRank: optimizes an upper bound of NDCG (Smooth version) CLiMF : optimizes a smooth version of MRR TFMAP: optimizes a smooth version of MAP AdaRank: uses boosting to optimize NDCG
  • 125. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to rank - Overview Proxy of Rankings Structured Estimation Non-smooth optimization Smoothing ranking measures ( Rating predicting methods CofiRank [Weimer 2007] WLT [Volkovs 2012] BPR [Rendle 2009] EigenRank [Liu 2008] TFMAP [Shi 2012a] pLPA [Liu 2009] CLiMF [Shi 2012b] ListRank [Shi 2010] GAPfm [Shi 2013a] CR [Balakrishnan 2012] XCLiMF [Shi 2013b] Classification by How to Model
  • 126. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ordrec [Koren 2011] Pointwise, proxy 11 22 33 44 55 t1 t2 t3 t4
  • 127. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ordrec [Koren 2011] Pointwise, proxy 11 22 33 44 55 t1 t2 t3 t4 Parameterize the threshold into: User factor, item factor and previous threshold Full distribution estimation
  • 128. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems BPR [Rendel 2009] Pair-wise, smoothing Main idea: learn by comparing items that the user likes with items that he does not. When dealing with implicit data sample the unseen data as negative Optimize the AUC, use a smooth version for Optimization purposes
  • 129. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems BPR [Rendel 2009] Pair-wise, smoothing Representation of pairwise preference data What Alice watched What Alice’s pairwise preferences (Row to Column) Transformers (T) ? Inception (I) ? Scent of a woman (S) 1 Forrest Gump (F) 1 Zombieland (Z) ? T I S F Z T ? - - ? I ? - - ? S + + ? + F + + ? + Z ? ? - -
  • 130. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ordrec [Koren 2011] Ratings are not numeric, but ordinal. Ordinal modeling by learning the thresholds Items rated similarly are similar with each other. An item rated 4 should be more similar to an item rated 5 rather than to an item rated 1 Rating 1 2 3 4 5
  • 131. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems i1 i2 i3 u1 2 3 4 u2 4 2 5 EigenRank [Liu 2008] Pair-wise, proxy Ranking-based similarity instead of rating based similarity User-user similarity based on Kendall Rank Correlation Neighbour selection for a user & item preference estimation Random walk with restart over pair-wise item preference graph
  • 132. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems List-wise Learn to model individual ratings CofiRank ListRank CLiMF TFMAP GAPfm xCLiMF Learn to model ranked list, or to model evaluation metrics that capture the quality of ranked list List-wise perspective
  • 133. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CoFiRank [Weimer 2007] list-wise, structured est. Based on Structured estimation i.e. Machine Learning methods for complex output domains (graphs, sequences, etc.) Cast the Ranking problem as a Structured estimation problem i.e. Learn a function that is maximized for the best possible ranking with respect to a ranking measure e.g. nDCG
  • 134. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CoFiRank [Weimer 2007] list-wise, structured est. Step 1: Instead of maximizing Minimize
  • 135. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CoFiRank [Weimer 2007] list-wise, structured est. Step 2:Step 2: Use Create a linear mapping That is maximized for Denote by a decreasing non-negative sequence
  • 136. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CoFiRank [Weimer 2007] list-wise, structured est. Step 3:Step 3: Find convex upper on non-convex optimization problems: Let Maximization by solving a Linear Assignment Problem to get an estimate for the worst case loss (maximum margin)
  • 137. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CoFiRank [Weimer 2007] list-wise, structured est. [3, 2 ,4 ,1] [4, 2 ,1 ,3] [1, 0.7 ,0.57 ,0.5] [-0.5, 0.0 ,0.42 ,0.1] 2.9 2.6 1.9 1.6 1.6 2.6 2.9 1.9
  • 138. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CoFiRank [Weimer 2007] list-wise, structured est.
  • 139. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CLiMF [Shi 2012b] List-wise, Smoothing Ranking Metrics Why optimize mean reciprocal rank (MRR)? Focus at the very top of the list Try to get at least one interesting item at the top of the list How to optimize MRR? Find a smooth version of MRR Find a lower bound of the smoother MRR
  • 140. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Graphical Notation RelevantRelevant Irrelevant Irrelevant Irrelevant Irrelevant Irrelevant RelevantRelevant RelevantRelevant
  • 141. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ranking using latent representation 55 44 11 44 22 11 22 11 [0.180, 0.019][0.180, 0.019] [0.013, 0.487][0.013, 0.487] [0.165, 0.632] [0.730, 0.109][0.730, 0.109] [0.420, 0.485] [0.463, 0.725] [0.894, 0.857] [0.942, 0.851] If user = [-100, -100] • 2d latent factor • We get the corresponding ranking
  • 142. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems 2 1 1 44 44 1 55 2 [0.894, 0.857] [0.463, 0.725] [0.942, 0.851] [0.730, 0.109][0.730, 0.109] [0.013, 0.487][0.013, 0.487] [0.165, 0.632] [0.180, 0.019][0.180, 0.019] [0.420, 0.485] Matrix Factorization (for ranking) • Randomly initialize item vectors • Randomly initialize user vectors • While not converged • Compute rating prediction error • Update user factors • Update item factors • Let's say user is [-100, -100] • Compute the square error (5-<[-100, -100], [0.180, 0.19]>)2=1764 • Update user & item factors to the direction where the error is reduced (according to the gradient of the loss) 8 items with ratings & random factors
  • 143. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning: Stochastic Gradient Descent with Square Loss
  • 144. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Square Loss User: [3, 1], RMSE=6.7 1 2 44 1 2 1 55 44 [0.942, 0.851] [0.894, 0.857] [0.730, 0.109][0.730, 0.109] [0.463, 0.725] [0.420, 0.485] [0.165, 0.632] [0.180, 0.019][0.180, 0.019] [0.013, 0.487][0.013, 0.487]
  • 145. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Example: average precision (AP) • AP: we compute the precision at each relevant position and average them
  • 146. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Why is hard? Non Smoothness Example: AP u:[-20,-20] u:[20,20]
  • 147. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems AP vs. RMSE The minimum in RMS and maximum in MAP do not correspond!
  • 148. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Non-smoothness of AP ymi =1 if item i is relevant for user m; else ymi = 0 The indicator function = 1 if it is true; else =0 rmi = rank of item i for user m
  • 149. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems AP vs. RMSE The minimum in RMS and maximum in MAP do not correspond!
  • 150. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to Rank for Top-k RecSys •Usually we care to make accurate ranking (and not rating) prediction •Square loss optimizes to accurately predict 1s and 5s. •RS should get the top k items right -> Ranking problem •Why not to learn how to rank directly? •Learning to Rank methods provide up to 30% performance improvements in off-line evaluations •It is possible, but a more complex task
  • 151. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems How can we get a smooth AP? • We replace non smooth places of MAP with smooth approximation
  • 152. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems How can we get a smooth MAP? • We replace non smooth places of MAP with smooth approximation
  • 153. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Smooth version of MAP u:[-20,-20] u:[20,20]
  • 154. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Not always the case: only sometimes approximation is very good…
  • 155. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ranking Inconsistencies Achieving a perfect ranking for all users is not possible Two Sources of Inconsistencies: ● Factor Models (all models) have limited expressive power and cannot learn the perfect ranking for all users Ranking functions approximations are inconsistent e.g. A > B & B > C but C > A
  • 156. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Summary on Ranking 101
  • 157. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Ranking Metrics: AUC RR AP nDCG
  • 158. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems AUC: Area Under the ROC Curve
  • 159. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems RR: Reciprocal Rank
  • 160. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems AP: Average Precision
  • 161. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems AP vs.RR
  • 162. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems nDCG: Normalized Discounted Cumulative Gain
  • 163. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems AP AUC RR Square Loss
  • 164. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Top-k Ranking Focus on the very top of the recommendation list since users pay attention only to the first k items Top heavy ranking measures put much more emphasis at the top of the list (e.g. MRR, MAP, NDCG). Drop in measure non-linear with the position in the list AUC gives the same emphasis to the top as to the bottom of the list. Drop in measure linear to the position in the list
  • 165. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to Rank for RecSys
  • 166. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to Rank for IR & ML Query-document (Q-D) retrieval: Given a query, rank documents. RankSVM LambdaRank RankNet RankBoostAdaR ank ListNet SVM-MAP SoftRank ... Features of a Q-D pair, relevance label Learn a ranking function Predict relevances of new Q-D pairs Analogy: Query-doc --- User-item
  • 167. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Learning to Rank for RecSys Focus on the very top of the recommendation list since users pay attention only to the first k items Top heavy ranking measures put much more emphasis at the top of the list (e.g. MRR, MAP, NDCG). Drop in measure non-linear with the position in the list AUC gives the same emphasis to the top as to the bottom of the list. Drop in measure linear to the position in the list
  • 168. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems The non-smoothness of Reciprocal Rank Reciprocal Rank (RR): The inverse of the rank of the first relevant item in a given list. Rank-based component Rank-based component
  • 169. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Reciprocal Rank
  • 170. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Lower Bound Jensen’s inequality Concavity of log function
  • 171. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems What's the key? CLiMF reciprocal rank loss essentially pushes relevant items apart In the process at least one items ends up high-up in the list
  • 172. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Conventional Loss
  • 173. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems CLiMF MRR-Loss
  • 174. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Optimizing RR vs. AUC Opt. RR Opt. AUC
  • 175. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems List-wise xCLiMF [Shi 2013b] – Generalization to Ratings Expected reciprocal rank (ERR) [Chapelle 2009] A generalization of RR to graded relevance data Cascade nature compared to NDCG Equal contribution independent of the items ranked above Different contributions relative to the items ranked above NDCG ERR 0 1 0 3 0 0 5 0 3 0 0 1 0 3 0 0 5 0 3 0 = <
  • 176. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems xCLiMF vs. CLiMF Results from Tuenti video watching dataset xCLiMF shows its advantage over CLiMF when applied to graded relevance data
  • 177. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems List-wise TFMAP [Shi 2012a] – Generalization to Context Context-aware Recommendation
  • 178. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Problem Setting Given: Users’ implicit feedback on items under different contexts Target: To recommend a list of items to each user under any given context, as accurate as possible Top-N recommendation Context-aware Optimal in terms of MAP Items Users Contexts
  • 179. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems TFMAP: Main concept CP tensor factorization U, V, C are latent factors (parameters to be learned) U, V, C are learned from optimizing the smoothed MAP
  • 180. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Social and Trust-based recommenders A social RS recommends items that are “popular” with the friends of the user. Friendship though does not imply trust “trust” in social-based RS can be per-user or topic-specific
  • 181. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Trust Inference Select 2 users: source (node A) & sink (C) Recommend to the source how much to trust the sink. source sink
  • 182. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Major Algorithms - Networks Advogato (Levien) Appleseed (Ziegler and Lausen) MoleTrust (Massa and Avesani) TidalTrust (Golbeck)
  • 183. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Building Recommender Systems Using Trust Use trust as a way to give more weight to some users Trust for collaborative filtering Use trust in place of (or combined with) similarity Trust for sorting and filtering Prioritize information from trusted sources
  • 184. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Social Recommendations
  • 185. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Other ways to use Social Social connections can be used in combination with other approaches In particular, “friendships” can be fed into collaborative filtering methods in different ways e.g. replace or modify user-user “similarity” by using social network information
  • 186. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 187. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Hybridization Methods Hybridization Method Description Weighted Outputs (scores or votes) from several techniques are combined with different degrees of importance to offer final recommendations Switching Depending on situation, the system changes from one technique to another Mixed Recommendations from several techniques are presented at the same time Feature combination Features from different recommendation sources are combined as input to a single technique Cascade The output from one technique is used as input of another that refines the result Feature augmentation The output from one technique is used as input features to another Meta-level The model learned by one recommender is used as input to another
  • 188. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Weighted Rating for an item is computed as the weighted sum of ratings produced by a pool of different RS. The weights are determined by training and get adjusted as new ratings arrive. Assumption: relative performance of the different techniques is uniform. Not true in general: e.g. CF performs worse for items with few ratings. e.g. P-Tango RS: a CB and a CF recommender equally weighted at first. Weights are adjusted as predictions are confirmed or not. RS with consensus scheme: each recommendation of a specific item counts as a vote for the item.
  • 189. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Feature Combination CF ratings of users are passed as additional feature to a CB. CB makes recommendations over this augmented data set.
  • 190. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Switching The system uses a criterion to switch between techniques The main problem is to identify a good switching criterion. e.g. The DailyLearner system uses a CB-CF. When CB cannot predict with sufficient confidence, it switches to CF.
  • 191. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Mixed Recommendations from more than one technique are presented together e.g. The PTV system recommends a TV viewing schedule for the user by combining recommendations from a CB and a CF system. CB uses the textual descriptions of TV shows; vs CF uses other users’ preferences. When collision occurs, the CB has priority.
  • 192. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Cascade At each iteration, a first recommendation technique produces a coarse ranking & a second technique refines the recommendation Cascading avoids employing the second, lower-priority, technique on items already well-differentiated by the first Requires a meaningful ordering of the techniques. E.g.: EntreeC is a restaurant RS uses its knowledge of restaurants to make recommendations based on the user’s stated interests. The recommendations are placed in buckets of equal preference, and the collaborative technique breaks ties
  • 193. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Feature Augmentation Very similar to the feature combination method: Here the output of one RS is incorporated into the processing of a second RS e.g.: Amazon.com generates text data (“related authors” and “related titles”) using its internal collaborative systems Libra system makes content-based recommendations of books based on these text data found in Amazon.com, using a naive Bayes text classifier
  • 194. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 195. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Beyond Explicit Ratings Implicit feedback is more readily available, and less noisy Already many approaches (e.g. SVD++) can make use of implicit feedback Ongoing research in combining explicit and implicit feedback D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In Proc.of the 18th WWW, 2009. Koren Y and J. Sill. OrdRec: an ordinal model for predicting personalized item rating distributions. In Rec-Sys ’11, pages 117–124, 2011. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD, 2008. Yifan Hu, Y. Koren, and C. Volinsky. Collaborative Filtering for Implicit Feedback Datasets. In Proc. Of the 2008 Eighth ICDM, pages 263–272, 2008.
  • 196. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Personalized Learning to Rank Better approaches to learning to rank that directly optimize ranking metrics and allow for personalization (e.g. CliMF & TFMAP) Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proc. of the sixth Recsys, 2012. Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson,A. Hanjalic, and N. Oliver. TFMAP: optimizing MAP for top-n context-aware recommendation. In Proc. Of the 35th SIGIR, 2012.
  • 197. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Context-aware Recommendations Beyond the traditional 2D user-item space Recommendations should also respond to user context (e.g. location, time of the day...) Many different approaches such as Tensor Factorization or Factorization Machines A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proc. of the fourth ACM Recsys, 2010. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context- aware recommendations with factorization machines. In Proc. of the 34th ACM SIGIR, 2011.
  • 198. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems User choice and presentation effects We log the recommended items to the users and their choice We can use this information as negative feedback (not chosen) and positive feedback (chosen). S.H. Yang, B. Long, A.J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. In Proc. of the 34th ACM SIGIR, 2011.
  • 199. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Social Recommendations Beyond trust-based Cold-starting with Social Information Combining Social with CF Finding “experts” J. Delporte, A. Karatzoglou, T. Matuszczyk, S. Canu. Socially Enabled Preference Learning from Implicit Feedback Data. In Proc. of ECML/PKDD 2013 N. N. Liu, X. Meng, C. Liu, and Q. Yang. Wisdom of the better few: cold start recommendation via representative based rating elicitation. In Proc. of RecSys’11, 2011. M. Jamali and M. Ester. Trustwalker: a random walk model for combining trust-based and item-based recommendation. In Proc. of KDD ’09, 2009. J. Noel, S. Sanner, K. Tran, P. Christen, L. Xie, E. V. Bonilla, E. Abbasnejad, and N. Della Penna. New objective functions for social collaborative filtering. In Proc. of WWW ’12, pages 859–868, 2012. X. Yang, H. Steck, Y. Guo, and Y. Liu. On top-k recommendation using social networks. In Proc. of RecSys’12, 2012.
  • 200. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Diversity in Recommendation (I) Recommendations from a music on-line retailer: No diversity: pop albums from female singers. Some are redundant. Born This Way Pink Friday Dangerously in Love Born This Way – The Remix Femme Fatale Can't be Tamed Teenage Dream Lady Gaga Nicki Minaj Beyoncé Lady Gaga Britney Spears Miley Cyrus Katy Perry
  • 201. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Diversity in Recommendation (II) Some good music recommendations: Different authors and genres. Not similar between them. These are much better recommendations! Wrecking Ball Not your Kind of People Like a Prayer Choice of Weapon Sweet Heart Sweet Light The Light the Dead See Little Broken Hearts B. Springsteen Garbage Madonna The Cult Spiritualized Soulsavers Norah Jones
  • 202. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Re-Ranking Recommender Re-ranking top 5 not diverse D top 5 diverse Ziegler et al. 2005 Zhang et al. 2008 Vargas et al. 2011 comedy drama action
  • 203. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Sub-Profiles comedy action drama
  • 204. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Novelty and Diversity Metrics
  • 205. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 206. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Conclusions Recommender Systems (RS) are an important application of Machine Learning RS have the potential to become as important as Search is now However, RS are more than Machine Learning HCI Economical models ...
  • 207. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Conclusions RS are fairly new but already grounded on well-proven technology Collaborative Filtering Machine Learning Content Analysis Social Network Analysis … However, there are still many open questions and a lot of interesting research to do!
  • 208. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Index 1. Introduction: What is a Recommender System? 2. Approaches 1. Collaborative Filtering 2. Content-based Recommendations 3. Context-aware Recommendations 4. Other Approaches 5. Hybrid Recommender Systems 3. Research Directions 4. Conclusions 5. References
  • 209. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems "Recommender Systems Handbook." Ricci, Francesco, Lior Rokach, Bracha Shapira, and Paul B. Kantor. (2010). “Recommender systems: an introduction”. Jannach, Dietmar, et al. Cambridge University Press, 2010. “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”. G. Adomavicious and A. Tuzhilin. 2005. IEEE Transactions on Knowledge and Data Engineering, 17 (6) “Item-based Collaborative Filtering Recommendation Algorithms”, B. Sarwar et al. 2001. Proceedings of World Wide Web Conference. “Lessons from the Netflix Prize Challenge.”. R. M. Bell and Y. Koren. SIGKDD Explor. Newsl., 9(2):75–79, December 2007. “Beyond algorithms: An HCI perspective on recommender systems”.K. Swearingen and R. Sinha. In ACM SIGIR 2001 Workshop on Recommender Systems “Recommender Systems in E-Commerce”. J. Ben Schafer et al. ACM Conference on Electronic Commerce. 1999- “Introduction to Data Mining”, P. Tan et al. Addison Wesley. 2005 References
  • 210. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems “Evaluating collaborative filtering recommender systems”. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. ACM Trans. Inf. Syst., 22(1):5–53, 2004. “Trust in recommender systems”. J. O’Donovan and B. Smyth. In Proc. of IUI ’05, 2005. “Content-based recommendation systems”. M. Pazzani and D. Billsus. In The Adaptive Web, volume 4321. 2007. “Fast context-aware recommendations with factorization machines”. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt- Thieme. In Proc. of the 34th ACM SIGIR, 2011. “Restricted Boltzmann machines for collaborative filtering”.R. Salakhutdinov, A. Mnih, and G. E. Hinton.In Proc of ICML ’07, 2007 “Learning to rank: From pairwise approach to listwise approach”.Z. Cao and T. Liu. In In Proceedings of the 24th ICML, 2007. “Introduction to Data Mining”, P. Tan et al. Addison Wesley. 2005 References
  • 211. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Balakrishnan, S., & Chopra, S. (2012, February). Collaborative ranking. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 143-152). ACM. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005, August). Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (pp. 89-96). ACM. Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., & Li, H. (2007, June). Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning (pp. 129-136). ACM. Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009, November). Expected reciprocal rank for graded relevance. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 621-630). ACM. Joachims, T. (2002, July). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133-142). ACM. Koren, Y. (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434). ACM. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. Koren, Y., & Sill, J. (2011, October). OrdRec: an ordinal model for predicting personalized item rating distributions. In Proceedings of the fifth ACM conference on Recommender systems (pp. 117-124). ACM. Liu, N. N., & Yang, Q. (2008, July). Eigenrank: a ranking-oriented approach to collaborative filtering. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 83-90). ACM Liu, N. N., Zhao, M., & Yang, Q. (2009, November). Probabilistic latent preference analysis for collaborative filtering. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 759-766). ACM. Quoc, C., & Le, V. (2007). Learning to rank with nonsmooth cost functions. Proceedings of the Advances in Neural Information Processing Systems, 19, 193-200. Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009, June). BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp. 452-461). AUAI Press. References
  • 212. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Salakhutdinov, R., Mnih, A., & Hinton, G. (2007, June). Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning (pp. 791-798). ACM. Shi, Y., Larson, M., & Hanjalic, A. (2010, September). List-wise learning to rank with matrix factorization for collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 269-272). ACM. Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., & Oliver, N. (2012a, August). TFMAP: Optimizing MAP for top- n context-aware recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (pp. 155-164). ACM. Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., & Hanjalic, A. (2012b, September). CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems (pp. 139-146). ACM. Taylor, M., Guiver, J., Robertson, S., & Minka, T. (2008, February). Softrank: optimizing non-smooth rank metrics. In Proceedings of the international conference on Web search and web data mining (pp. 77-86). ACM.. Volkovs, M., & Zemel, R. S. (2012). Collaborative ranking with 17 parameters. In Advances in Neural Information Processing Systems (pp. 2303-2311). Weimer, M., Karatzoglou, A., Le, Q. V., & Smola, A. J. (2007). Cofi rank-maximum margin matrix factorization for collaborative ranking. In Advances in neural information processing systems (pp. 1593-1600). Xu, J., & Li, H. (2007, July). Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 391-398). ACM. Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007, July). A support vector method for optimizing average precision. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 271-278). ACM. References
  • 213. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Recsys Wiki: http://recsyswiki.com/ Recsys conference Webpage: http://recsys.acm.org/ Recommender Systems Books Webpage: http://www.recommenderbook.net/ Mahout Project: http://mahout.apache.org/ MyMediaLite Project: http://www.mymedialite.net/ Online resources
  • 214. MLSS Sydney, February 18, 2015 – Machine Learning for Recommender Systems Thank you! Questions? Alexandros Karatzoglou alexk@tid.es @alexk_z