1. + Social Bookmarking and Collaborative Filtering Christopher G. Wagner

2. + What is Social Bookmarking? Bookmark storage Online storage vice locally in a browser No folders Items can belong to more than one “folder” Finding others with similar interests Using interests of others to locate more interesting sites

3. + Views of Social Bookmarks View personal bookmarks and tags View all items with a particular tag(s) New way of searching View tags of another user Create private and public groups for sharing View ratings of bookmarks

4. + Joshua Schacter’s del.icio.us

5. + Joshua’s ‘math’ Tag

6. + The ‘math’ Tag

7. + The del.icio.us Interface

8. + My del.icio.us

9. + A del.icio.us Network

10. + The ‘for:’ Tag

11. + Social Bookmarking Projects Del.icio.us Furl.net Flickr.com Simpy.com Gmail.com Clusty.com Stumbleupon.com IBM’s dogear

12. + What is Collaborative Filtering? “Collaborative filtering (CF) is the method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating).” -Wikipedia (http://en.wikipedia.org/wiki/Collaborative_filtering)http://en.wikipedia.org/wiki/Collaborative_filtering Take advantage of users’ input and behavior to make recommendations. “System for helping people find relevant content” -Rashmi Sinha (http://www.rashmisinha.com)http://www.rashmisinha.com

13. + Traditional Collaborative Filtering Each user represented by an N-dimensional vector, where N is the number of items Elements of vector can be ratings, or indicator of purchase, etc. Typically multiplied by the inverse frequency Use algorithm to measure similarity of vectors, e.g. cosine similarity

14. + Problems M customers, N items O(MN) is worst case Typically O(M+N) Still problematic when M,N ~ 10 6

15. + Cluster Models View customers as a classification problem Create clusters of customers Assign user to “nearest” cluster Base recommendations on user’s cluster

16. + Search Based Methods Construct searches based on keywords from user’s existing items Not practical if user has many items Recommendations tend to be poor

17. + Types of Collaborative Filtering Active Sending pointers to a resource User ratings Passive Observing user behavior Item Based Items become the focus, not users

18. + Active Collaborative Filtering Uses a peer-to-peer approach Users want to actively share information, recommendations, evaluations, ratings, etc.  Usually, information is from a user who has direct experience with the product  Biased opinions  Less data available

19. + Netflix Queue

20. + Netflix Ratings

21. + Netflix Recommendations

22. + Netflix Prize October 2, 2006 - October 2, 2011 Improve their recommendation system by at least 10% over the current method $1M Grand Prize $50k Yearly Prizes

23. + Passive Collaborative Filtering Monitor user’s activity Purchasing item Repeated use of an item Number of times queried Makes use of implicit filters  Requires nothing additional from users  Doesn’t capture user’s evaluation

24. + Google’s Sponsored Links www.AreYouASlackerMom.com www.royalsaharajasper.com Related to Pi Mu Epsilon “Will pay stipend to Grad” “Cheap Faculty Flights” “Greek Ringtone”

25. + Google’s Personalized Search

26. + Item-to-Item Collaborative Filtering Focus is on finding similar items, not similar customers Originally proposed by Vucetic and Obradovic in 2000 Matches user’s items to similar items to create recommendations Association Rule Mining

27. + Amazon Slide Similar to impulse items in checkout line Tailored to each user

28. + Amazon’s Recommendations

29. + Amazon’s Similar Items

30. + Amazon’s Algorithm For each item in product catalog, I 1 For each customer C who purchased I 1 For each item I 2 purchased by customer C Record that a customer purchased I 1 and I 2 For each item I 2 Compute the similarity between I 1 and I 2 Only items purchased by common customer are compared, not all pairs of items

31. + Run Time of Algorithm Worst case O(N 2 M) In practice, more like O(NM) Is run offline, so it does not affect customer For customer, you only have to aggregate items similar to their purchases and make recommendations, which is fast

32. + Collaborative Filtering With Tags User input is usually a barrier, not so with tags User’s bookmarks reveal information about their interests, which is useful for finding others of similar interests Applications to corporate repositories of information (IBM’s dogear) Both active (tags) and passive (logs) filtering

33. + References G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, 2003, pp. 76-80. R. Sinha, “Collaborative Filtering strikes back (this time with tags)”, http://www.rashmisinha.com/archives/05_10/tags-collaborative-filtering.html. http://www.rashmisinha.com/archives/05_10/tags-collaborative-filtering.html S. Vucetic and Z. Obradovic, “A Regression-Based Approach for Scaling-Up Personalized Recommender Systems in E-Commerce,” Workshop on Web Mining for E-Commerce, at the 6th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (KDD), Boston, MA, 2000.A Regression-Based Approach for Scaling-Up Personalized Recommender Systems in E-Commerce R. Wash and E. Rader, “Collaborative Filtering with del.icio.us”, working paper.Collaborative Filtering with del.icio.us R. Wash and E. Rader, “Incentives for Contribution in del.icio.us: The Role of Tagging in Information Discovery”, working paper.Incentives for Contribution in del.icio.us: The Role of Tagging in Information Discovery Wikipedia, “Collaborative Filtering”, http://en.wikipedia.org/wiki/Collaborative_filtering. http://en.wikipedia.org/wiki/Collaborative_filtering