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A Glimpse of Recommender Systems on the Web. Bin Tan 4/26/07. Classification. Method: content-based, collaborative, hybrid Collaborative filtering: user-to-user, item-to-item User & item representation: id, keywords, category, metadata, context (demographics, time, location, social network)
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A Glimpse of Recommender Systems on the Web Bin Tan 4/26/07
Classification • Method: content-based, collaborative, hybrid • Collaborative filtering: user-to-user, item-to-item • User & item representation: id, keywords, category, metadata, context (demographics, time, location, social network) • Feedback: explicit, implicit
Amazon.com • Item-to-item collaborative filtering • Feedback collected from purchases, ratings and page views • User profile editable • Efficient • Amazon.com recommendations: item-to-item collaborative filtering
Amazon.com Highlights • Customers with Similar Searches Purchased … • What Do Customers Buy After Viewing This Item? • Better Together Buy this item with … today! • Customers who bought this item also bought … • Explore similar items: more like this / by category • Customers viewing this page may be interested in these Sponsored Links • Rate this item to improve your recommendations • Today’s Recommendation For You • Category Tags • Improve Your Recommendations • Update your Amazon history to improve your recommendations
Findory.com (52,002) • Personalized News • Clickthrough as feedback
LibraryThing.com (8,939) • Personal library management • Add a book by searching catalogs of 70 online libraries • Share book rating, tags, reviews • Find people with similar books • Get book recommendations
Other Popular Sites • Last.fm (music) 350 • iLike.com (music) 2,322 • RateYourMusic.com 5,463 • FilmAffinity.com 7,443 • Douban.com (books, movies, music) 1,485
StumbleUpon • Firefox / IE plug-in • Recommend web pages, photos, videos, news • Feedback: user-selected categories, item rating (thumb-up/down) • Social network features
References • Wikipedia: Collaborative Filtering • Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions