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, rating

s and page views User profile editable Efficient Amazon.com recommendations: item-to-it

em 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, n

ews Feedback: user-selected categories, item r

ating (thumb-up/down) Social network features

References

Wikipedia: Collaborative Filtering Toward the Next Generation of Recomme

nder Systems: A Survey of the State-of-the-Art and Possible Extensions