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

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We live in information overload!

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“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!

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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

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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

BellKor´s Pragmatic Chaos

10.5% improvement in July 2009

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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

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User Profiling Understand people´s 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

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Technology overview

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RECOMMENDERS

Collaborative filtering(CF)

Content-basedFiltering (CB)

Hybridrecommenders

Memory-basedCF Algorithms

Model-basedCF Algorithms

Collaborative filtering (CF)

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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 user’s ratings

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

Memory-based CF Algorithms

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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

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User-based vs. Item-based

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

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

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Model-based CF Algorithms

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RECOMMENDERS

Collaborative filtering (CF)

Content-basedFiltering (CB)

Hybridrecommenders

Memory-basedCF Algorithms

Model-basedCF Algorithms

Model-Based CF Algorithms

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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

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all ratings

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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

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Content-based recommenders

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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 user’s preferences are

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LargeSImilarity

Small Similarity

Limitations and problems of CB

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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

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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

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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

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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

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SummaryTechniques Advantages Limitations

Co

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ora

tive

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

• easy implementation• no content considered

•data sparsity•cold start problem•limited 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• overspecialization•new 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

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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.

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