recommender systems

CMPT 884, SFU, Martin Ester, 1-09 1

Recommender Systems

Martin EsterSimon Fraser University

School of Computing Science

CMPT 884Spring 2009


Recommender Systems

Outline• Introduction

motivation, applications, issues• Collaborative filtering

user-based, item-based, challenges• Trust-based recommendation deterministic, random walks, challenges• Model-based recommendation

[Konstan 2008] [Cohen 2002]


Recommender SystemsIntroduction

• search engine users just type in a few keywords• search engine overwhelms user with a flood of results• ranking mechanism based on similarity between

query keywords and web pages and on prestige of pages• search engine‘s answers do not take into account user feedback and users‘ preferences Information needs more complex than keywords or topics: quality and taste


Recommender SystemsIntroduction

• Users are not willing to spend a lot of time to specify their personal information needs• Recommender systems automatically identify relevant information or products relevant for a given user, learning from available data • Data can be transactions of all users / customers of a website or profile of an individual user

users who bought this book also bought . . . (Amazon.com)


Recommender SystemsPersonalization Level

• Genericeveryone receives same recommendations

• Demographicmatches a demographic group

•Personalizedmatches an individual, everybody gets different recommendations

• Ephemeralmatches current activity

• Persistentmatches long-term interests


Recommender Systems

Types of Systems

• Filtering interfacesE-mail filters, clipping services

• Recommendation interfacessuggestion lists, “top-n,” offers and promotions

• Prediction interfacesevaluate candidates, predicted ratings


Recommender Systems

Collaborative Filtering• Main idea users rate items

users are correlated with other userspersonal predictions for unrated items

• Nearest-Neighbor Approachfind people with history of agreementaggregate their ratings to predict rating of userassume stable tastes

employs data about the target user and other users


Recommender Systems

TargetTargetuseruser

Aggregator

Prediction


Recommender Systems

Collaborative Filtering


Recommender Systems

Collaborative Filtering•Recommendation task 1

Predicting the rating on a target item for a given user Predicting John’s rating on Star Wars Movie

movie1 ??Recommender


Recommender Systems

Collaborative Filtering•Recommendation task 2

Recommending a list of items to a given user Recommending a list of movies to John for

watchingList of Top Movies ??

Recommender


Recommender Systems

Applications

•Movie recommendations

•Book recommendations

•Recommendation of friends


Recommender Systems

Privacy and Trustworthiness• Who knows what about me?

– personal information revealed– identity

• Is the recommendation honest?– biases built-in by operatore.g. want to sell „old hats“ or prefers ads with higher bids

• Vulnerability to external manipulation (fraud)- insert fraudulent user profiles which rate my producthighly



Introduction

Rating Matrix

Users

Items

Ratings

What is Joe’s rating of Blimp and of RockyXV?

Similar user



Example



Definitions

• vi,j: vote of user i on item j

• Ii = items for which user i has voted• mean vote of user i is• predicted vote for active user a on target item j is weighted sum of votes on j by n “similar” users

normalizer weights of n similar users



Definitions

• K-nearest neighbor

• Pearson correlation coefficient

• Cosine distance

else0

)neighbors( if1),(

aiiaw



Evaluation [Herlocker 2004]•split users into train/test sets•for each user a in the test set:

- split a’s votes into observed (I) and to-predict (P)

- measure average absolute deviation between predicted and actual votes in P

- alternatively, measure the squared deviation predicted and actual votes in P

•average error measure over all test users MAE or RMSE



Evaluation•There is a trade-off between precision and recall•Measure also the recall / coverage,

i.e. the percentage of (a,i) pairs for which methodcan make a recommendation

•F-measure considers both precision and recall

Max squared error



Evaluation

• so far, only comparison against ground truth• in industry, want to measure the business profit• user surveys • in an online system

measure click through ratesmeasure add-on sales



Challenges

• user item rating matrix is very sparsetypically 99% of the entries unknown

dimensionality reduction item-item based CF• cannot make (accurate) recommendations for cold start users users who have recently joined the system and have rated only very few items (typically, 50% of users) trust-based recommendation



Challenges

• the larger the user community- the more variance among the ratings- the more the ratings converge to the mean

value cluster users and use only the corresponding cluster

to make a recommendation • cannot compute the confidence of a recommendation

system does not know its limits probabilistic methods•vulnerable to fraud copy a user profile and become the most similar user trust-based recommendation



Challenges

• need to explain recommendations• how to reward serendipity in the evaluation?

recommendations should not all be of the same kind• how to evaluate a set of recommendations?• how to produce the best sequence of recommendations?



leads to a denser rating, lower-dimensional matrix can alternatively use Singular Value Decomposition (SVD) or Latent Semantic Indexing (LSI)



Item-Item Collaborative Filtering [Sarwar et al 2001]

• Many applications have many more users (customers)than items (products)

• Many customers have no similar customers• Most products have similar products• Make recommendation by considering ratings of active user for similar products



Item-Item Collaborative Filtering

Aggregator

Prediction



Explanations • Simple visual representations of neighbors ratings

• Statement of strong previous performance “MovieLens has predicted correctly 80% of the time for you”


Collaborative FilteringExplanations

• Complex representations are not accepted by users, e.g.

- more than one dimension- any use of statistical

terminology such ascorrelation, variance, etc.


Trust-based Recommendation

Introduction

•Users tend to trust ratings given by their trusted friends•Trust is propagated in the social network•Trust is transitive (to a certain degree)

and asymmetric•Use neighborhood of (directly or indirectly) trusted friends to find reliable ratings and make a recommendation• Can make recommendations for cold start users

as long as they are somehow connected to the network•More robust to fraud



Introduction



ara userofratingaverage:

Definitions

•ri,j: rating of user i for item j •Trust network:

graph G = (U,T) where U is a set of nodes (users) and T is a set of edges (trust relationships)•Edges can be weighted, but typically they are not•Trust relationships can be explicitly stated by users (e.g., Epinions.com) or be implicitly derived from observed interactions between users (e.g., MSN network)

otherwise0,

Tv)(u, if,1,vut



Definitions

• for users i and j which are connected via T, the indirect trust between i and j is defined via some trust model, based on the direct trust values• raters: all users that have rated target item i•trusted raters: all raters that are trusted by active user u (to a certain degree)



uaw ua userinuseroftrust:,

Definitions

•

and f is a function comuting the trust model

•recommendation by aggregating the ratings of k trusted raters u

T})),( and T),(|),({(f

Tu)(a, if,

,,

,, uvvatt

tw

uvva

uaua



Issues

•How to compute the indirect trust?•How many of the trusted raters to consider?•Which ones?•If using too few, the prediction is not based on a significant number or rates. If using too many, these raters may only be weakly trusted.•In a large trust network, need to consider also the efficiency of exploring the trust network.



TidalTrust [Golbeck 2005]

• most accurate information will come from the highest trusted neighbors•in principle, each node should consider only its neighbors with highest trust rating• but different nodes have different max trust among their neighbors, which would lead to different levels of trust in different parts of the network• max: largest trust value such that a path can be found from source to sink with all tij >= max• define indirect trust recursively



MoleTrust [Massa et al 2007]

• trust model similar to TidalTrust• major difference in the set of trusted raters considered• both, TidalTrust and MoleTrust perform a breadth-first search of the trust network• TidalTrust considers all raters at the minimum depth (shortest path distance from the active user)• MoleTrust considers all raters up to a specified maximum depth



Discussion

• TidalTrust is likely to find only very few raters• MoleTrust may consider too many raters• TidalTrust ignores the actual ratings and their distribution• MoleTrust even ignores the actual distribution of the raters maximum depth independent of a and i



Random Walks [Andersen et al 2008]

•perform a random walk in the trust network starting from user a•if current user u has rating for item i, return it•otherwise, choose a trusted neighbor v randomly with probability proportional to tu,v and go to v•terminate as soon as rating found or some specified maxdepth reached• repeat random walks until the average aggregated rating converges •use the aggregated rating as recommendation termination depends on distribution of raters and ratings



Experimental Evaluation

•Epinions datasetproducts rated on a scale of [1. . 5]explicit trust network (binary)

epinions.com•Distinguish cold start users and all users•Comparison of various CF and trust-based methods•Item based 0 / .4 / .8: considers only items with similarity

at least 0 / .4 / .8•Random Walk 1 / 6: considers trusted raters up to depth 1 / 6




•all trust-based methods greatly improve the coverage of CF methods•they also have very competitive RMSE




•all methods perform much better on all users than on cold start users only•the gain of trust-based methods is not so significant


Model-based Recommendation

Introduction [Cohen 2002]

• so far: memory-based methodsCF, trust-based recommendation

• no training of a model•model-based approaches to CF:

1) CF as density estimation2) CF and content-based recommendation as classification



CF as Density Estimation [Horvitz et al 1998]

• estimate Pr(Rij=k) for each user i, movie j, and rating k• use all available data to build model for this estimator

RijAirplane Matrix Room with a

View... Hidalgo

Joe 9 7 2 ... 7

Carol 8 ? 9 … ?

... ... ... ... ... ...

Kumar 9 3 ? … 6



CF as Density Estimation

•a simple model

same model for all users

jkRkR

R

jikRi

kRj

ijk

ij

ij

ijij

movie of rating average)Pr(]E[

:unknown for valueexpected this toLeads

) rating users(#) : users(#

)Pr( , movies



CF as Density Estimation

•a more complex modelgroup users into M “clusters”: c(1), ..., c(M)

same model for all users within a group

mij

ijm

ij

mcjmciR

mcimcikRkR

))(in of rating average())(Pr(][E

))(Pr())(|Pr()Pr(

estimate by counts



CF as Density Estimation• group users into clusters using Expectation-Maximization:

- randomly initialize Pr(Rm,j=k) for each m i.e., initialize the clusters differently somehow- E-Step: estimate Pr(user i in cluster m) for each i,m- M-Step: find maximum likelihood (ML) estimator for Rij

within each cluster muse ratio of #(users i in cluster m with rating Rij=k) to #(user i in cluster m ), weighted by Pr(i in m) from E-step

- repeat E-step, M-step until convergence



CF as Classification [Basu et al, 1998]

• Classification task: map (user,movie) pair into {likes,dislikes}• Training data: known likes/dislikes, test data: active users • Features: any properties of user/movie pair

Airplane Matrix Room with a View

... Hidalgo

comedy action romance ... action

Joe 27,M,70k 1 1 0 1

Carol 53,F,20k 1 1 0

...

Kumar 25,M,22k 1 0 0 1

Ua48,M,81k 0 1 ? ? ?



CF as Classification• e.g., moviesLikedByUser(Joe) = {Airplane,Matrix,...,Hidalgo} age(Joe)=27, income(Joe)=70k, genre(Matrix)=action, director(Matrix) = . . Airplane Matrix Room with a

View... Hidalgo


Joe 27,M,70k 1 1 0 1

Carol 53,F,20k 1 1 0

...

Kumar 25,M,22k 1 0 0 1

Ua48,M,81k 0 1 ? ? ?



CF as Classification


... Hidalgo


Joe 27,M,70k 1 1 0 1

Carol 53,F,20k 1 1 0

...

Kumar 25,M,22k 1 0 0 1

Ua48,M,81k 0 1 ? ? ?

genre={romance}, age=48, sex=male, income=81k, usersWhoLikedMovie={Carol}, moviesLikedByUser={Matrix,Airplane}, ...





... Hidalgo


Joe 27,M,70k 1 1 0 1

Carol 53,F,20k 1 1 0

...

Kumar 25,M,22k 1 0 0 1

Ua48,M,81k 0 1 ? ? ?

genre={action}, age=48, sex=male, income=81k, usersWhoLikedMovie = {Joe,Kumar}, moviesLikedByUser={Matrix,Airplane},...



CF as Classification• Classification algorithm RIPPER (rule learner)• Sample classification rules

if NakedGun33/13 moviesLikedByUser(U) and Joe usersWhoLikedMovie(M) and genre(M)=comedy then likes(U,M)

if age(U)>12 and age(U)<17 and HolyGrail moviesLikedByUser(U) and director(M) =MelBrooks then likes(U,M)

if Ishtar moviesLikedByUser(U) then likes(U,M)



CF as Classification• features - collaborative: UsersWhoLikedMovie, UsersWhoDislikedMovie, MoviesLikedByUser - content: Actors, Directors, Genre, MPAA rating, ... - hybrid: ComediesLikedByUser, DramasLikedByUser, UsersWhoLikedFewDramas, ...•predict liked(U,M) for the M in top quartile of U’s ranking for different feature sets• evaluate recall and precision w.r.t. actual (U,M) pairs




•precision at same level of recall (about 33%)

•RIPPER with collaborative features only performs worse than memory-based CF

by about 5 pts precision (73% vs. 78%)

• RIPPER with hybrid features performs better than memory- based CF

by about 5 pts precision (83% vs. 78%)


Recommender SystemsReferences

•R. Andersen, C. Borgs, J. Chayes, U. Feige, A. Flaxman, A. Kalai, V.

Mirrokni, and M. Tennenholtz: Trust-based recommendation systems: an axiomatic approach, WWW 2008• Chumki Basu, Haym Hirsh, and William W. Cohen: Recommendation as Classification: Using Social and Content-Based Information in Recommendation, AAAI 1998•William Cohen: Collaborative Filtering, Tutorial DIMACS Workshop, 2002•Jennifer Golbeck: Computing and Applying Trust in Web-based Social Networks, PhD Thesis, University of Maryland College Park, 2005•J. Herlocker et al.: Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, Jan. 2004


Recommender SystemsReferences

• Eric Horvitz, Jack S. Breese, David Heckerman, David Hovel, Koos Rommelse: The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users, UAI 1998• Joseph A. Konstan: Introduction to Recommender Systems, Tutorial SIGMOD 2008• Paolo Massa, Paolo Avesani: Trust-aware Recommender Systems, ACM RecSys 2007•Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John Riedl: GroupLens: An Open Architecture for Collaborative Filtering of Netnews, ACM Conference on Computer Supported Cooperative Work, 1994• Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl: ItemBased Collaborative Filtering Recommendation Algorithms, WWW 2001

recommender systems

Documents

rating of user

target user

individual user users

active user

fraudulent user profiles

account user feedback

mean vote of user i

users customers