Toward the next generation of recommender systems

Toward the Next generation of
Recommender systems
2008. 11.05
IEEE Transactions on Knowledge and Data Engineering
Volume 17 , Issue 6 (June 2005)
Written by Gediminas Adomavicius, Alexander Tuzhilin
Summarized by Gihyun Gong

Copyright ©2008 by CEBT
About paper
 This paper is about an overview of recommendation
system
 Focused on rating based recommendation which is most
popular
 Content based
 Collaborative filtering
 Hybrid methods
 Extending capabilities of recommendation system

Outline
 About recommendation
 Recommendation methods
 Demographic filtering
 Content-based Methods
 Collaborative Methods
 Hybrid Methods
 Current research issues in recommendation system

Recommendation
 Recommendation is type of information filtering technique
that attempts to present information items (movies, music,
books, news, images, web pages) that are likely of interest
to the user
 Recommendation can be formulated as :
C : all users
S : set of all possible item
u : function that measures the usefulness of item s to user c
 Recommendation is reduced to the problem of
estimating ratings for the items that have not been seen by a
user
 How to rating?
 How to estimating?

Recommendation (cont’d)
 Problem of recommender system
 Usually not defined on the whole C X S space, but only on
some subset of it
 Recommendation engine should be able to estimate the
ratings of the non-rated movie/user

Recommendation system
 Recommendation system is a system which has the effect of
guiding the user in a personalized way to interesting or useful
objects in a large space of possible options
 Recommender systems are usually classified into the following
categories, based on how recommendations are made:
 Demographic filtering
 Content-based recommendations: The user will be recommended items
similar to the ones the user preferred in the past
 Collaborative recommendations: The user will be recommended items
that are preferred by other people with similar tastes and
preferences
 Hybrid approaches: These methods combine collaborative and content-
based methods.

Demographic filtering
 Uses demographic information
 Ages, Jobs, Location, …
 Advantages
 No feedback is needed
 No cold start problem
 Disadvantages
 Can not provide personalization
 Low accuracy
 Too general

Content-based recommendation
 Recommend items similar to those users preferred in
the past
 User preference profile is the key
 Matching “user preferences” with “item characteristics”
 Designed mostly to recommended text-based items
 The content in these system is usually described with
keywords
 Similarity measure
 TF-IDF
 Cosine similarity

Similarity function
 TF-IDF
 N is the number of documents
 Ni is How many times keyword ki is appears in the document
 Fi,j is the number of times keyword ki is appears in the document j
 Cosine Similarity
 For text matching, the attribute vectors A and B are usually the tf-idf vectors of the
documents.
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Limitation of Content-based method
 Limited Content Analysis
 This method is based on text, but not all content is well
represented by keywords
 Picture, Taste, …
 Overspecialization
 User is limited to being recommended items already rated
 Unrated items not shown
 Use random or mutation in genetic algorithm to solve
 New User Problem
 This method uses user preference profile
 New user have very few ratings (or no history available)
 System needs new user’s rating of sample items
 However, people usually do not want to rate sample items

Collaborative Filtering
 Using Trend information, 『 Word of Mouth 』
 Basic idea of CF
1. Build a ratings table from user rating.
2. Compare user’s ratings, and calculate similarity between
users.
We call the user group which presents high similarity that ‘Nearest
Neighborhood’
3. Predict user preference based on rating of Nearest
neighborhood.

Collaborative Filtering methods
 Memory-based (or Nearest-Neighborhood)
 Similarity based model
 Use entire collection of previously rate item by the user
 Store all user information in a Database
 Model-based
 Probabilistic model
 Use collection of rating to learn a model, which is used
to make rating prediction
 Based on machine-learning
 Bayesian network, Clustering, NN, …

Advantages of Collaborative Filtering
 Can deal with multimedia contents
 Can recommend based on user preference and quality of
item
 Can recommend serendipity item

Limitation of Collaborative method
 New User Problem
 Must first learn the user’s preferences from the ratings that the user
gives
 New Item Problem
 Until the new item is rated by a substantial number of users, the
recommender system would not be able to recommend it
 User’s rating problem
 Different users might use different scales
 Sparsity
 The number of ratings already obtained is usually very small compared to
the number of ratings that need to be predicted
 Scalability
 Computing cost grows with C X S space
 System typically have to search millions of users and items, it causes a
serious scalability problem
 However, these correlations will change when new users are added
 Adaptability
 Requirement of a user may change over time

Surveys on Hybrid method
 Combining separate recommender
 Linear combination of two outputs
 Voting scheme
 Adding Content-based to Collaborative model
 Add Content-based profile for each user
 Use filterbot, the virtual user
 Adding Collaborative to Content-based model
 Add user profiles presented by term vector for each items
 Single unifying model
 Knowledge-based techniques
 Entrée uses some domain knowledge
 Quickstep, Foxtrot system uses topic ontology

Extending capabilities
 Comprehensive understanding of Users and Items
 Profiles in pure content-based and collaborative-based
still tend to be quite simple and do not utilize some of
the more advanced profiling techniques
 In addition to using traditional profile features, such
as keywords and simple user demographics more advanced
profiling techniques based on data mining rules,
sequences, and signatures that describe a user’s interests
can be used to build user profiles

17
Extending capabilities (cont’d)
 Multidimensionality of Recommendations
 Current recommendation system uses only 2-dimension
 User x Item
 We can extend dimension of recommendation
 Context(TPOK), Demographic information, …

18
 Example of multidimension : The movie
 Traditional recommendation consider just 2 space
 Who is the user?
 What movie?
 We can consider other information
 Characteristics of the movie?
 Person wants to see movie?
 Where and how the movie will be seen?
 With whom the movie will be seen?
 When will the movie be seen?

 Multicriteria Rating
 To expand rating criteria
 Taking a linear combination of multiple criteria and
reducing the problem to a single-criterion optimization
problem
 Optimizing the most important criterion and converting
other criteria to constraint

 Restaurant example :

 Nonintrusiveness
 The problem of feedback normalizing
 One way to explore the intrusiveness problem is to
determine an optimal number of ratings the system should
ask from a new user
 This topic is related to Opinion Mining

 Flexibility
 Most of the recommendation methods are “hard-wired” into the
systems
 Therefore, the end-user cannot customize recommendations
according to his or her needs in real time.
 Also, most of the recommender systems recommend only
individual items to individual users and do not deal with
aggregation.
 However, it is important to be able to provide aggregated
recommendations in a number of applications, such as
recommend brands or categories of products to certain
segments of users (e.g. Vacations in Florida - Students).
 One way to support aggregated recommendations is by utilizing
the OLAP-based approach.
 Recommendation Query Language (RQL)

23
 RQL is SQL-like language for expressing flexible user-
specified recommendation requests
 “recommend to each user from New York the best three movies
that are longer than two hours” can be expressed in RQL”.

Toward the next generation of recommender systems

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