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Recommendation Systems

Recommendation Systems. By: Bryan Powell, Neil Kumar, Manjap Singh. R ecommendation system?. Information filtering technology Presents data on products that interests the user Algorithm uses previous user interactions. Recommendation System. Observes apparent user characteristics

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Recommendation Systems

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  1. Recommendation Systems By: Bryan Powell, Neil Kumar, Manjap Singh

  2. Recommendation system? • Information filtering technology • Presents data on products that interests the user • Algorithm uses previous user interactions Recommendation System

  3. Observes apparent user characteristics • Compares characteristics to an item • Predicts a rating the user would give to the item • Assigns the highest predicted item as a recommendation What does a recommendation system do exactly?

  4. General Recommendation Types • Personalized recommendation • based on the individual's past behavior • Social recommendation • based on the past behavior of similar users • Item recommendation • based on the item itself

  5. Amazon • Amazon used all 3 approaches (personalized, social and item). • Amazon’s recommendation system is very sophisticated

  6. ALL MIGHTY GOOGLE • Google uses its recommendation system every time a user searches through it. • Based on your location and/or recent search activity • When you're signed in to your Google Account, you “may see even more relevant, useful results based on your web history”

  7. Google Cont. • Google's search algorithm is called PageRank. • Dependent on social recommendations (i.e. who links to a webpage) • Google also does item recommendations with its “Did you mean” feature.

  8. Who uses Recommendation Systems? • Content Sites • eCommerce Sites • Advertisment

  9. Content Sites • Task: • predict ratings of items by a given user • find a list of interesting items • Data: • content description • explicit rating for some user • Examples: AlloCine, Zagat, LibraryThing, Last.fm, Pandora, StumbleUpon Recommendation for a user on LibraryThing

  10. eCommerceSites • Task: • build group of products for bundle sales • find a list of products that a user is likely to buy • Data: • list of purchases • browsing history for all users • Example: • Amazon • Netfix

  11. The Recommendation Giant http://www.netflix.com/

  12. eCommerceSites Cont. • Netflix Prize • $1 million prize given in 2009 • Sought to substantially improve Netflix’s method of predictions for users

  13. eCommerce Sites Cont. • Netflix Challenge Cont. • The BellKor’s Pragmatic Chaos team improved Netflix’s recommendation system by 10.06 % BellKor's Pragmatic Chaos

  14. eCommerce Sites Cont. • (Netflix Cont.) The BellKor’sPragmatic Chaos team had a lower score than the 2nd place team (The Ensemble) The Belkor’s Pragmatic Chaos team: (10.06%) The Ensemble: (10.06%) The Belkor’s Pragmatic Chaos only won because they submitted their code 20 minutes before The Ensemble. .856714 .856714

  15. Advertisement • Task: • find a list of advertisements optimized according to expected income • Data: browsing history for all users • Example: Google AdSense, DoubleClick

  16. Common Approaches to Recommendation Systems • Content Filtering Algorithms • Collaborative Filtering Algorithms • Hybrid Methods • K-Nearest Neighbor Approach

  17. Content Filtering Algorithms • Algorithm based on attributes of items and ratings of the user • Interprets the preferences of a user as a function of attributes • Two main types of C.F.A.: • Heuristic – Based • Model Based

  18. Content Filtering Algorithms Cont. • Heuristic Based • Uses common types of information retrieval • TF/ ID • Cosine • Clustering • Model Based • Uses a probabilistic model to learn the predictions of a user

  19. Collaborative Filtering • Filters information/patterns using different sources • Involves very large data sets • Filters what the user sees based on tastes • Steps: • Look for users who share similar rating patterns • Calculate predictions for user from other ratings • Amazon invented item-based collaborative filtering

  20. Collaborative Filtering Cont.

  21. Hybrid Methods • Uses both item attributes and the ratings of all users • Hybrid methods were made to cope with the conventional recommendation system • Two main types of C.F.A.: • Heuristic – Based • Model Based

  22. Hybrid Methods Cont. • Heuristic Based • Uses both content filtering and collaborative filtering methods • Aims to get the best from both algorithms • Model Based • Model is modified in order to take into account both types of data

  23. K-Nearest Neighbor Approach • Classified based on a majority of its neighbors • Classifies Objects based on closest training examples • Computation deferred until classification  instance-based learning • Can be used for regression and utilizes Euclidean distances • Larger “k” values reduce noise on classification • They make boundaries between classification less distinct

  24. Additional Resources Netflix Prize-http://www.netflixprize.com//community/viewtopic.php?id=1537 uPenn- http://www.cis.upenn.edu/~ungar/CF/

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