CMPT 884, SFU, Martin Ester, 1-09 104 Recommender Systems Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009

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

Recommender Systems

Martin Ester

Simon Fraser University

School of Computing Science

CMPT 884

Spring 2009


Recommender Systems

Outline

• Introductionmotivation, applications, issues

• Collaborative filtering user-based, item-based, challenges

• Trust-based recommendation deterministic, random walks, challenges

• Model-based recommendation

[Konstan 2008] [Cohen 2002]


Recommender Systems

Introduction

• 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 Systems

Introduction

• 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 Systems

Personalization Level• Generic

everyone receives same recommendations• Demographic

matches a demographic group•Personalized

matches an individual, everybody gets different recommendations

• Ephemeralmatches current activity

• Persistentmatches long-term interests


Recommender Systems

Types of Systems

• Filtering interfaces

E-mail filters, clipping services

• Recommendation interfaces

suggestion lists, “top-n,” offers and promotions

• Prediction interfaces

evaluate candidates, predicted ratings


Recommender Systems

Collaborative Filtering

• Main idea

users rate items

users are correlated with other users

personal predictions for unrated items

• Nearest-Neighbor Approach

find people with history of agreement

aggregate their ratings to predict rating of user

assume stable tastes employs data about the target user and other users


Recommender Systems

TargetTargetuseruser

AggregatorAggregator

Prediction


Recommender Systems



Recommender Systems


•Recommendation task 1Predicting the rating on a target item for a given user

Predicting John’s rating on Star Wars Movie

movie1 ??Recommender


Recommender Systems


•Recommendation task 2 Recommending a list of items to a given user

Recommending a list of movies to John for watching

List 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 product

highly



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 errorMax 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 sparse

typically 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

AggregatorAggregator

Prediction



Explanations

• Simple visual

representations of

neighbors ratings

• Statement of strong previous

performance “MovieLens has

predicted correctly 80%

of the time for you”



Explanations

• Complex representations

are not accepted by users, e.g.

- more than one dimension

- any use of statistical

terminology such as

correlation, 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 dataset

products 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 methods

CF, trust-based recommendation

• no training of a model

•model-based approaches to CF:

1) CF as density estimation

2) 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 estimatorRij

Airplane 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

ji

kRikRj

ijk

ij

ij

ijij

movie of rating average)Pr(]E[

:unknown for valueexpected this toLeads

) rating users(#

) : users(#)Pr( , movies




•a more complex model

group 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




• 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 m

use 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


... 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 ? ? ?





... 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},...




• 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)




• 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

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CMPT 884, SFU, Martin Ester, 1-09 104 Recommender Systems Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009