Recommender Systems
This presentation is the property of its rightful owner.
Sponsored Links
1 / 56

Recommender Systems PowerPoint PPT Presentation


  • 204 Views
  • Uploaded on
  • Presentation posted in: General

Recommender Systems. Martin Ester Simon Fraser University School of Computing Science CMPT 884 Spring 2009. Recommender Systems. Outline Introduction motivation, applications, issues Collaborative filtering user-based, item-based, challenges

Download Presentation

Recommender Systems

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Recommender systems

Recommender Systems

Martin Ester

Simon Fraser University

School of Computing Science

CMPT 884

Spring 2009

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems

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]

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems1

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 betweenquery 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

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems2

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)

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems3

Recommender Systems

  • Personalization Level

  • • Generic

  • everyone receives same recommendations

  • • Demographic

  • matches a demographic group

  • Personalizedmatches an individual, everybody gets different recommendations

  • • Ephemeralmatches current activity

  • • Persistent

  • matches long-term interests

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems4

Recommender Systems

  • Types of Systems

  • Filtering interfaces

  • E-mail filters, clipping services

  • Recommendation interfaces

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

  • Prediction interfacesevaluate candidates, predicted ratings

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems5

Recommender Systems

  • Collaborative Filtering

  • Main idea

  • users rate items

  • users are correlated with other userspersonal predictions for unrated items

  • Nearest-Neighbor Approach

  • find people with history of agreementaggregate their ratings to predict rating of userassume stable tastes

  •  employs data about the target user and other users

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems6

Target

user

Recommender Systems

Aggregator

Prediction

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems7

Recommender Systems

  • Collaborative Filtering

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems8

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

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems9

Recommender Systems

  • Collaborative Filtering

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems10

Recommender Systems

  • Applications

  • Movie recommendations

  • Book recommendations

  • Recommendation of friends

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems11

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

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering

Collaborative Filtering

Rating Matrix

  • Introduction

Items

Users

Ratings

Similar user

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

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering1

Collaborative Filtering

  • Example

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering2

Collaborative Filtering

  • 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 usera on target itemj is weighted sum of votes on j by n “similar” users

normalizer

weights of n similar users

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering3

Collaborative Filtering

  • Definitions

  • K-nearest neighbor

  • Pearson correlation coefficient

  • Cosine distance

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering4

Collaborative Filtering

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering5

Collaborative Filtering

  • 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 method

  • can make a recommendation

  • F-measure considers both precision and recall

Max squared error

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering6

Collaborative Filtering

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering7

Collaborative Filtering

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering8

Collaborative Filtering

  • 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 clusterto make a recommendation

  • cannot compute the confidence of a recommendationsystem does not know its limits probabilistic methods

  • vulnerable to fraud copy a user profile and become the most similar user  trust-based recommendation

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering9

Collaborative Filtering

  • 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?

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering10

Collaborative Filtering

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering11

Collaborative Filtering

 leads to a denser rating, lower-dimensional matrix

 can alternatively use Singular Value Decomposition (SVD) or Latent Semantic Indexing (LSI)

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering12

Collaborative Filtering

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering13

Collaborative Filtering

Item-Item Collaborative Filtering

?

Aggregator

Prediction

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering14

Collaborative Filtering

Explanations

• Simple visual representations of

neighbors ratings

• Statement of strong previous performance “MovieLens has

predicted correctly 80%

of the time for you”

CMPT 884, SFU, Martin Ester, 1-09


Collaborative filtering15

Collaborative Filtering

  • Explanations

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

    • more than one dimension

    • any use of statistical

    • terminology such ascorrelation, variance, etc.

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation

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 usersas long as they are somehow connected to the network

  • More robust to fraud

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation1

Trust-based Recommendation

Introduction

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation2

Trust-based Recommendation

  • 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)

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation3

Trust-based Recommendation

  • 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)

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation4

Trust-based Recommendation

  • Definitions

  • and f is a function comuting the trust model

  • recommendation by aggregating the ratings of k trusted raters u

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation5

Trust-based Recommendation

  • 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.

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation6

Trust-based Recommendation

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation7

Trust-based Recommendation

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation8

Trust-based Recommendation

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation9

Trust-based Recommendation

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation10

Trust-based Recommendation

  • 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 similarityat least 0 / .4 / .8

  • Random Walk 1 / 6: considers trusted raters up to depth 1 / 6

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation11

Trust-based Recommendation

Experimental Evaluation

  • all trust-based methods greatly improve the coverage of CF methods

  • they also have very competitive RMSE

CMPT 884, SFU, Martin Ester, 1-09


Trust based recommendation12

Trust-based Recommendation

Experimental Evaluation

  • all methods perform much better on all users than on cold start users only

  • the gain of trust-based methods is not so significant

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation

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 estimation

  • 2) CF and content-based recommendation as classification

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation1

Model-based Recommendation

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

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation2

Model-based Recommendation

CF as Density Estimation

  • a simple model

  •  same model for all users

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation3

Model-based Recommendation

CF as Density Estimation

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

  •  same model for all users within a group

estimate by counts

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation4

Model-based Recommendation

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 Rijwithin 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

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation5

Model-based Recommendation

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: anyproperties of user/movie pair

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation6

Model-based Recommendation

CF as Classification

  • e.g., moviesLikedByUser(Joe) = {Airplane,Matrix,...,Hidalgo} age(Joe)=27, income(Joe)=70k, genre(Matrix)=action, director(Matrix) = . .

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation7

Model-based Recommendation

CF as Classification

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

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation8

Model-based Recommendation

CF as Classification

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

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation9

Model-based Recommendation

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)

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation10

Model-based Recommendation

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

CMPT 884, SFU, Martin Ester, 1-09


Model based recommendation11

Model-based Recommendation

CF as Classification

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

  • RIPPER with collaborative features only performs worse than memory-based CFby about 5 pts precision (73% vs. 78%)

  • RIPPER with hybrid features performs better than memory- based CFby about 5 pts precision (83% vs. 78%)

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems12

Recommender Systems

  • References

  • 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

CMPT 884, SFU, Martin Ester, 1-09


Recommender systems13

Recommender Systems

  • References

  • 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

CMPT 884, SFU, Martin Ester, 1-09


  • Login