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  • Recommender Systems

    Simona Dakova

    Web Technologies Prof. Dr. Ulrik Schroeder WS 2010/111The slides are licensed under a

    Creative Commons Attribution 3.0 License

  • Overview Motivation

    Netflix Prize Competition

    Collaborative filtering approaches

    Content-based techniques

    Hybrid recommenders

    Summary

    Web Technologies2

  • We live in information overload!

    Web Technologies3

    We are leaving the age of Information and entering the Age of Recommendation -The Long Tail (Chris Anderson)

  • Netflix: 2/3 of the movies rented were recommended

    Google News: 38% more click-throughs

    Amazon: 35% sales from recommendations

    They try to attract you!

    Web Technologies4

  • Why recommenders? Enhance e-commerce and boost sales

    Browsers into buyers

    Recommender vs. Search:

    Discover the items you are looking, match your preferences

    Limited list of results

    Personalize your website content to the profile of an individual user

    Discover interesting items

    Automated personalization

    Increase usage and satisfaction

    Web Technologies5

  • Netflix Prize Competition $1.000.000 - if you only improve existing system by 10%!

    Contest started in 2006

    Annual progress prize $ 50.000

    Gained great popularity inacademic circles

    The Winner

    BellKors Pragmatic Chaos

    10.5% improvement in July 2009

    Web Technologies6

  • Recommender System = ? Definition:

    Algorithms/Systems for information filtering attempting to recommend certain items the user might like

    Items:

    Advertising messages, Investment choices, Restaurants, Cafes, Music tracks, Movies, TV programs, Books, Cloths, Supermarket goods, Tags, News articles, Online mates, Research papers

    Web Technologies7

  • User Profiling Understand peoples needs and interests

    Explicit Data Collection

    Ask for rating of items

    Rank a set of items

    Ask for detailed information/feedback

    CON: not well received by users, not ubiquitous

    Implicit Data Collection

    Purchasing history

    Items viewed

    Navigational patterns

    Obtain list of watched/listened items

    Analyze social data

    CON: Privacy concerns

    Web Technologies8

  • Technology overview

    Web Technologies9

    RECOMMENDERS

    Collaborative filtering(CF)

    Content-basedFiltering (CB)

    Hybridrecommenders

    Memory-basedCF Algorithms

    Model-basedCF Algorithms

  • Collaborative filtering (CF)

    Web Technologies10

    RECOMMENDERS

    Collaborative filtering (CF)

    Content-basedFiltering (CB)

    Hybridrecommenders

    Memory-basedCF Algorithms

    Model-basedCF Algorithms

    prediction based on past ratings

    compute similarities betweenusers/items

    make prediction according to thecalculated weight (similarity)

    learn a model from users ratings

    use the model to predict theprobabilistic rating of the activeuser on given item

  • Memory-based CF Algorithms

    Web Technologies11

    RECOMMENDERS

    Collaborative filtering (CF)

    Content-basedFiltering (CB)

    Hybridrecommenders

    Memory-basedCF Algorithms

    Model-basedCF Algorithms

  • Entire or sample of the user-item matrix

    Steps:

    1. For the active user/item identify his neighbors

    Similarity computation

    Pearson correlation

    Vector cosine-based similarity

    2. Neighborhood-based prediction/ Top-N Recommendation

    Memory-based CF Algorithms

    Web Technologies12

  • User-based vs. Item-based

    Web Technologies13

    User-based = You may like it because your friends liked it

    Item-based = You may like it because you like similar items

    i1 i2 i3 i4 i5

    u1 5 8 7 8

    u2 10 1

    u3 2 10 9 9

    u4 2 9 9 10

    u5 1 5 1

    ua 2 9 10

    i1 i2 i3 i4 i5

    u1 5 8 7 8

    u2 10 1

    u3 2 10 9 9

    u4 2 9 9 10

    u5 1 5 1

    ua 2 9 10

  • Model-based CF Algorithms

    Web Technologies14

    RECOMMENDERS

    Collaborative filtering (CF)

    Content-basedFiltering (CB)

    Hybridrecommenders

    Memory-basedCF Algorithms

    Model-basedCF Algorithms

  • Model-Based CF Algorithms

    Web Technologies15

    Train your system to recognize complex patterns in user-

    item data (ratings)

    Make the recommendation based on the trained model

    Relies on machine learning and data mining algorithms

    Train

    r11

    r9

    r8r7

    r6

    r5

    r4

    r1

    r3

    r2

    all ratings

    r8

    r7 r4

    r3

    MODEL(only set of ratings)

    RECOMMENDATION

  • Limitations and problems of CF Depend on human ratings

    Data sparsity

    Cold start , New user and New item problem

    Scalability

    Synonymy

    Shilling attacks

    Gray/Black sheep

    Web Technologies16

  • Content-based recommenders

    Web Technologies17

    RECOMMENDERS

    Collaborative filtering(CF)

    Content-basedFiltering (CB)

    Hybridrecommenders

    Memory-basedCF Algorithms

    Model-basedCF Algorithms

  • Content-based recommendation (CB)

    For items containing textual information (keywords)

    Information Retrieval

    Compares similarity of the features of given items

    Example: Movie recommendation application

    Analyze common features among the movies

    Recommend only the movies that have a high degree of similarity to whatever the users preferences are

    Web Technologies18

    LargeSImilarity

    Small Similarity

  • Limitations and problems of CB

    Web Technologies19

    Limited content analysis

    Explicitly associated features

    Multimedia data relies on tagging

    Same set of features indistinguishable

    Overspecialization

    Difficult to recognize synonyms, concepts, or new emerging words

    New user Problem

  • Hybrid recommenders

    Web Technologies20

    RECOMMENDERS

    Collaborative filtering(CF)

    Content-basedFiltering (CB)

    Hybridrecommenders

    Memory-basedCF Algorithms

    Model-basedCF Algorithms

  • Collaborativefiltering

    Hybrid recommenders Use combination of CF and CB

    Implementing methods separately and combining their predictions

    Incorporating CB characteristics into a CF approach or vice versa

    Constructing a general unifying model that incorporates both

    Example: content-boosted collaborative filtering

    Web Technologies21

    i1 i2 i3 i4

    u1 5 8 x 7

    u2 10 x 1 x

    u3 2 x 10 9

    u4 x 2 9 9

    ua 2 x 9 10

    i1 i2 i3 i4

    u1 5 8 7 7

    u2 10 4 1 8

    u3 2 5 10 9

    u4 6 2 9 9

    ua 2 3 9 10

    RECOMMENDATION

    Contentpredictor

  • Pros/Cons of Hybrid Recommenders Advantages

    Address limitations of pure CF or CB systems

    Provide more accurate recommendations

    Performance improvement

    Overcome sparsity

    Disadvatages

    Comlexity

    Expensive to build

    Web Technologies22

  • The winning solution on Netflix Contest

    A blend of several complex

    algorithms into a hybrid recommender system

    Main improvement:

    Incorporate temporal effects that cause movie and user biases as well as the changing user preferences

    Web Technologies23

  • SummaryTechniques Advantages Limitations

    Co

    llab

    ora

    tive

    Memory-based algorithms: Neighborhood-based CF Top-N recommendation

    easy implementation no content considered

    data sparsitycold start problemlimited scalability

    Model-based algorithms: machine learning / data mining algorithms

    deal better with sparsity, scalability intuitive rationale

    expensive modeling trade-off between performance and scalability

    Co

    nte

    nt-

    bas

    ed Information retrieval no data about other users recommendation for new/unpopular items predictions for users with unique tastes

    limited content analysis overspecializationnew user problem

    Hyb

    rid

    s

    combination of collaborative and content-based approaches

    overcome limitations of pure collaborative and content-based recommendations more accurate recommendations performance improvement

    complexity expensive to build

    Web Technologies24

  • Literature

    Adomavicius, G., Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions.

    Su, X., Khoshgoftaar, T. 2009 A Survey on Collaborative Filtering Techniques.

    Sarwar, B., Karypis, G., Konstan, J., Riedl, J. 2001 Item-based collaborative Filtering Recommendation Algorithms.

    Das, A., Datar, M., Garg, A. 2007 Google News Personalization: Scalable Online Collaborative Fitlering.

    Linden, G., Smith, B., York, J. 2003 Amazon.com Recommendations Item-to-Item Collaborative Filtering.

    Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Erel, U. 2010 Social Media Recommendation based on People and Tags.

    Schafer, J., Konstan, J., Riedl, J. 1999 Recommender Systems in E-Commerce.

    http://www.irelaxa.com/Geecat/2010/09/16/recommendation-system-collaborative-filtering/

    Piotte, M., Chabbert, M. 2009 Extending the toolbox.

    Web Technologies25