1. A Glimpse of Recommender Systems on the Web
Bin Tan
4/26/07

2. 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

3. 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

4. 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

5. Findory.com (52,002)
Personalized News
Clickthrough as feedback

6. 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

7. 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

8. StumbleUpon Firefox / IE plug-in
Recommend web pages, photos, videos, news
Feedback: user-selected categories, item rating (thumb-up/down)
Social network features

9. References Wikipedia: Collaborative Filtering
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions