Lecture 5: Personalization on the Social Web (2014)

Social Web
2014
Lecture V: Personalization on the Social Web
(some slides adopted from Fabian Abel)
Lora Aroyo
The Network Institute

VU University Amsterdam

theory & techniques for

how to design & evaluate

recommenders & user models

to use in Social Web applications

Social Web 2014, Lora Aroyo!

Fig. 1 Functional model of tasks and sub-tasks speciﬁcally suited for SASs

Fig. 1 Functional model of tasks and sub-tasks speciﬁcally suited for SASs (Ilaria Torre, 2009)

User Modeling

How to infer & represent

user information that supports a given
application or context?
Kevin Kelly


User Modeling Challenge
• Application has to obtain,

understand & exploit information
about the user

• Information (need & context)
about user

• Inferring information about user &
representing it so that it can be
consumed by the application

• Data relevant for inferring
information about user


User & Usage Data
is Everywhere
• People leave traces on the Web and on their computers:
• Usage data, e.g., query logs, click-through-data

• Social data, e.g., tags, (micro-)blog posts, comments,
bookmarks, friend connections

• Documents, e.g., pictures, videos

• Personal data, e.g., afﬁliations, locations

• Products, applications, services - bought, used, installed

• Not only a user’s behavior, but also interactions of other users
• “people can make statements about me”

• “people who are similar to me can reveal information about me”

• “social learning” collaborative recommender systems

UM: Basic Concepts
• User Profile = data structure = a characterization of a user at a
particular moment
represents what, from a given
(system) perspective, there is to know about a user. The data
in the profile can be explicitly given by user or derived by
system

• User Model = definitions & rules for the interpretation of

observations about the user & about the translation of that
interpretation into the characteristics in a user profile
user model is the recipe for obtaining & interpreting user profiles

• User Modeling = the process of representing the user

User Modeling Approaches
• Overlay User Modeling: describe user characteristics, e.g.

“knowledge of a user”, “interests of a user” with respect to
“ideal” characteristics

• Customizing: user explicitly provides & adjusts elements of
the user profile

• User model elicitation: ask & observe the user; learn &
improve user profile successively
modeling”

“interactive user

• Stereotyping: stereotypical characteristics to describe a user

• User Relevance Modeling: learn/infer probabilities that a given
item or concept is relevant for a user

Related scientific conference: http://umap2011.org/ Related journal: http:/umuai.org/

Which approach suits best
the conditions of
applications?

Social Web 2014, Lora Aroyo! http://farm7.staticﬂickr.com/6240/6346803873_e756dd9bae_b.jpg

Overlay User Models
• among the oldest user models

• used for modeling student
knowledge

• the user is typically characterized
in terms of domain concepts &
hypotheses of the user’s knowledge
about these concepts in relation
to an (ideal) expert’s knowledge

• concept-value pairs

User Model Elicitation
• Ask the user explicitly
learn

• NLP, intelligent dialogues

• Bayesian networks, Hidden Markov models

• Observe the user
learn

• Logs, machine learning

• Clustering, classiﬁcation, data mining 
• Interactive user modeling: mixture of direct inputs of a
user, observations and inferences


http://hunch.com

User
Stereotypes
•

set of characteristics (e.g.
attribute-value pairs) that
describe a group of users.

•

user is not assigned to a single
stereotype - user proﬁle can
feature characteristics of
several different stereotypes


http://farm1.staticﬂickr.com/155/413650229_31ef379b0b_b.jpg

based on slides from Fabien Abel

Can we infer a
Twitter-based User Profile?
Personalized News
Recommender
Profile

?

User Modeling
(4 building blocks)

Semantic Enrichment,
Linkage and Alignment


I want my
personalized news
recommendations!

User Modeling Building
Blocks

1. Temporal
Constraints

1. Which tweets of
the user should be
analyzed?

start

Profile?

concept weight

?

weekends

Morning:
Afternoon:
Night:

(a) time period
(b) temporal patterns

end

time
June 27

July 4


July 11

Blocks
Francesca
Schiavone

T Sport
concept weight

# hashtag-based
entity-based

T topic-based

#

2. Profile
Type

Profile?

Francesca Schiavone won
French Open #fo2010
French
Open

1. Temporal
Constraints

?

fo2010

2. What type of concepts
should represent “interests”?
time
June 27

July 4


July 11

Blocks
Francesca
Schiavone

1. Temporal
Constraints

(a) tweet-based
Profile?

Francesca Schiavone won!
http://bit.ly/2f4t7a

concept weight
Francesca
Schiavone
French Open
Tennis

Francesca wins French Open
Thirty in women's
tennis is primordially
old, an age when
agility and desire
recedes as the …

French
Open

2. Profile
Type
3. Semantic
Enrichment

(b) further enrichment

Tennis

3. Further enrich the semantics of tweets?

Blocks

1. Temporal
Constraints

Profile?

concept

4. How to weight the
concepts?
Concept frequency (TF)

TFxIDF
Time-sensitive

weight

Francesca
Schiavone

4

French Open
Tennis

?

weight(French Open)
weight(Francesca
Schiavone)

3
6

2. Profile
Type
3. Semantic
Enrichment
4. Weighting
Scheme

weight(Tennis)

time
June 27

July 4


July 11

Observations
• Profile characteristics:
• Semantic enrichment solves sparsity problems

• Profiles change over time: recent profiles reflect better
current user demands

• Temporal patterns: weekend profiles differ significantly
from weekday profiles

• Impact on recommendations:
• The more fine-grained the concepts the better the
recommendation performance: entity-based > topic-based
> hashtag-based

• Semantic enrichment improves recommendation quality

• Time-sensitivity (adapting to trends) improves
performance

User Modeling
it is not about putting everything in a user proﬁle

it is about making the right choices


User Adaptation
Knowing the user to adapt a system or interface

to improve the system functionality and user experience


User-Adaptive Systems
user
profile
user modeling

observations,
data and
information
about user

profile analysis

adaptation
decisions

A. Jameson. Adaptive interfaces and agents. The HCI handbook: fundamentals,
evolving technologies and emerging applications, pp. 305–330, 2003.

Last.fm adapts to
your music taste
user profile
interests in
genres,
artists, tags

user modeling
(infer current
musical taste)

compare profile
with possible next
songs to play

history of
songs, like,
ban, pause,
skip

next song to
be played


Issues in User-Adaptive
Systems
• Overﬁtting, “bubble effects”, loss of serendipity problem:

• systems may adapt too strongly to the interests/behavior

• e.g., an adaptive radio station may always play the same or
very similar songs

• We search for the right balance between novelty and
relevance for the user

• “Lost in Hyperspace” problem:

• when adapting the navigation – i.e. the links on which
users can click to ﬁnd/access information

• e.g., re-ordering/hiding of menu items may lead to
confusion

What is good user modelling
& personalisation?


http://www.ﬂickr.com/photos/bellarosebyliz/4729613108

Success Perspectives

• From the consumer perspective of an
adaptive system:

! Adaptive system maximizes
satisfaction of the user

hard to measure/obtain

!

• From the provider perspective of an
adaptive system:

Adaptive system maximizes
the profit


influence of UM &
personalization may be
hard to measure/obtain

Evaluation Strategies
• User studies: ask/observe (selected) people whether you did a
good job

• Log analysis: Analyze (click) data and infer whether you did a
good job,

• Evaluation of user modeling:

• measure quality of profiles directly, e.g. measure overlap with
•

existing (true) profiles, or let people judge the quality of the
generated user profiles

measure quality of application that exploits the user profile,
e.g., apply user modeling strategies in a recommender
system


Evaluating User Modeling
in RecSys
training data

test data (ground truth)

item C
item A
item B

training
data

item G
item E

item D

measure
quality

time

item F

Recommendations:
Z
X
Y

Strategy X
Strategy Y

item H

Recommender

?

User Modeling strategies to compare

item H

item F

item H

Strategy Z

item R

item G

item H

?

?

?

item M

item N

item M

Possible Metrics
• The usual IR metrics:

• Precision: fraction of retrieved items that are relevant

• Recall: fraction of relevant items that have been retrieved

• F-Measure: (harmonic) mean of precision and recall

• Metrics for evaluating recommendation (rankings):

• Mean Reciprocal Rank (MRR) of ﬁrst relevant item

• Success@k: probability that relevant item occurs within the
top k

• If a true ranking is given: rank correlations

• Precision@k, Recall@k & F-Measure@k

• Metrics for evaluating prediction of user preferences:

• MAE = Mean Absolute Error

• True/False Positives/Negatives

performance


strategy X
baseline

runs
Is strategy X better than the baseline?

Example Evaluation
• [Rae et al.] a typical example of how to investigate and evaluate a proposal for
improving (tag) recommendations (using social networks)

• Task: test how well the different strategies (different tag contexts) can be used
for tag prediction/recommendation

• Steps:

1. Gather a dataset of tag data part of which can be used as input and aim to
test the recommendation on the remaining tag data

2. Use the input data and calculate for the different strategies the predictions

3. Measure the performance using standard (IR) metrics: Precision of the top
5 recommended tags (P@5), Mean Reciprocal Rank (MRR), Mean Average
Precision (MAP)

4. Test the results for statistical signiﬁcance using T-test, relative to the
baseline (e.g. existing approach, competitive approach)

[Rae et al. Improving Tag Recommendations Using Social Networks, RIAO’10]]

Example Evaluation
• [Guy et al.] another example of a similar evaluation approach

• The different strategies differ in the way people & tags are
used: with tag-based systems, there are complex
relationships between users, tags and items, and strategies
aim to ﬁnd the relevant aspects of these relationships for
modeling and recommendation

• The baseline is the ‘most popular’ tags - often used to

compare the most popular tags to the tags predicted by a
particular personalization strategy - investigating whether
the personalization is worth the effort and is able to
outperform the easily available baseline.

[Guy et al. Social Media Recommendation based on People and Tags, SIGIR’10]]

Recommendation Systems
Predict relevant/useful/interesting items

for a given user (in a given context)

it’s often a ranking task


March 28, 2013


http://www.wired.com/magazine/2011/11/mf_artsy/all/1


Collaborative Filtering
• Memory-based: User-Item matrix: ratings/preferences of users => compute
similarity between users & recommend items of similar users

• Model-based: Item-Item matrix: similarity (e.g. based on user ratings) between
items => recommend items that are similar to the ones the user likes

• Model-based: Clustering: cluster users according to their preferences =>
recommend items of users that belong to the same cluster

• Model-based: Bayesian networks: P(u likes item B | u likes item A) = how likely
is it that a user, who likes item A, will like item B learn probabilities from
user ratings/preferences

• Others: rule-based, other data mining techniques

u1

likes

likes

u2

likes


! u1 likes
Pulp Fiction?

Memory vs. Model-based
• complete input data is
required

• pre-computation not
possible

• does not scale well

• high quality of
recommendations

!

• abstraction (model) of input
data

• pre-computation (partially)
possible (model has to be
re-built from time to time)

• scales better

• abstraction may reduce
recommendation quality


Social Networks &
Interest Similarity
• collaborative ﬁltering: ‘neighborhoods’ of people with similar interest
& recommending items based on likings in neighborhood

• limitations: next to ‘cold start’ and ‘sparsity’ the lack of control (over

one’s neighborhood) is also a problem, i.e. cannot add ‘trusted’ people, nor
exclude ‘strange’ ones

• therefore, interest in ‘social recommenders’, where presence of social

connections deﬁnes the similarity in interests (e.g. social tagging CiteULike):

• does a social connection indicate user interest similarity?

• how much users interest similarity depends on the strength of their
connection?

• is it feasible to use a social network as a personalized
recommendation?

[Lin & Brusilovsky, Social Social Web 2014, Lora Aroyo! Similarity: The Case of CiteULike, HT’10]
Networks and Interest

Conclusions
• unilaterally connected pairs have more common items/metadata/tags than non-connected pairs

• highest similarity for direct connections - decreasing with the increase of distance between users in SN

• reciprocal relationship users - significantly larger similarity than users in a unidirectional relationship

• traditional item-level similarity may be less reliable to find similar users in social bookmarking systems

• peers connected by self-defined social connections could be a useful source for cross-recommendation


Content-based
Recommendations
• Input: characteristics of items & interests of a user into

characteristics of items => Recommend items that feature
characteristics which meet the user’s interests

• Techniques:

• Data mining methods: Cluster items based on their
•
•

characteristics => Infer users’ interests into clusters

IR methods: Represent items & users as term vectors =>
Compute similarity between user proﬁle vector and items

Utility-based methods: Utility function that gets an item as
input; the parameters of the utility function are
customized via preferences of a user

Government stops
renovation of tower
bridge Oct 13th 2011

Tower Bridge
is a combined bascule and suspension
bridge in London, England, over the
River Thames.
Category: politics, england
Related Twiper news:
@bob: Why do they stop to… [more]
@mary: London stops reno… [more]

Tower Bridge today Under construction

Content
Features

db:Politics
db:Sports
db:Education
db:London
db:Tower_Bridge
db:Government
db:UK

Weighting strategy:
-  occurrence frequency
-  normalize vectors (1-norm ! sum of vector equals 1)

0.2
0
0
0.2 = a
0.4
0.1
0.1

User’s Twitter
history
RT: Government stops
renovation of tower
bridge Oct 13th 2011
I am in London at the
moment Oct 13th 2011
I am doing sports
Oct 12th 2011

User Model
db:Politics
db:Sports
db:Education
db:London
db:Tower_Bridge
db:Government
db:UK

Weighting strategy:
-  occurrence frequency (e.g. smoothened by occurrence time ! recent
concepts are more important
-  normalize vectors (1-norm ! sum of vector equals 1)

0
0.1
0
0.5 = u
0.2
0.2
0

db:Politics
db:Sports
db:Education
db:London
db:Tower_Bridge
db:Government
db:UK

candidate
a
b
0.2 0
0
0
0
0
0.2 0.8
0.4 0.2
0.1 0
0.1 0

items user
c
u
0
0
0.5
0.1
0.2
0
0
0.5
0
0.2
0
0.2
0.3
0
cosine
similarities

Recommendations


u

a

b

c

0.67 0.92 0.14

Ranking of recommended items:
1.  b
2.  a
3.  c

RecSys Issues
• Cold-start problem (new user problem): no/little data available to infer preferences of new users
• Changing User Preferences: user interests may change over time
• Sparsity problem (new item problem): item descriptions are sparse, e.g. not many user rated or
tagged an item

• Lack of Diversity (overﬁtting): when adapting too strongly to the preferences of users they might see
same/similar recommendations

• Use the right context: users do things, which might not be relevant for their user model, e.g. try out
things, do stuff for other people

• Research challenge: right balance between serendipity & personalization
• Research challenge: right way to use the inﬂuence of recommendations on user’s behavior


Hands-on Teaser
• Your Facebook Friends’ popularity in a spread sheet

• Locations of your Facebook Friends

• Tag Cloud of your wall posts

image
Social Web 2014, Lora Aroyo! source: http://www.ﬂickr.com/photos/bionicteaching/1375254387/

Lecture 5: Personalization on the Social Web (2014)

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Lecture 5: Personalization on the Social Web (2014)