Recommenders Everywhere: The WikiLens Community-Maintained Recommender System

1. Recommenders Everywhere: The WikiLens Community-Maintained Recommender System Dan Frankowski, Shyong K. (Tony) Lam, Shilad Sen, F. Maxwell Harper, Scott Yilek, Michael Cassano, John Riedl University of Minnesota

3. Beer lover Many beers The Whole Talk in One Slide How can we help him decide which beers to drink?

4. WikiLens

5. Outline • Motivation • Principles • System Design • Experiences • Possible Improvements

6. Some People Love Sharing

7. Some People Love Sharing

8. Some People Love Sharing

9. Some People Love Sharing YouTube craigslist eBay …

10. Information overload! She could use a recommender system… Finding What You Want

11. What Is a Recommender? • A personalized recommender recommends items based on your personal preferences • Amazon: “If you like A, you might like B” (because 80% of people who bought A also bought B) • Combining your As => personalized list of Bs • Uses collaborative filtering algorithms, e.g., • combining ratings of users like you • combining ratings of items similar to those you rate • Requires many users and many ratings

12. A Recommender System • movielens.org • Started by GroupLens in 1995 • 120K users (several thousand active in a given month) • 9K movies • 13M ratings • No beer. 

13. Tools for community-maintained sites • Suppose our beer lover wants to start a community site • Wikis (many – MediaWikis, editme.com) • Forums (millions – phpBB) • Blogs (many millions – technorati tracks 108M) • How to start a recommender for beer? • Fueled by community contribution? • We propose community-maintained recommenders, where users contribute all the content and information needed to recommend content

14. Small-world recommenders • Traditional recommender algorithms need large: many users, many ratings • Most online communities are small • We propose small-world recommenders • Provide value with little data per item • Depend on users to understand other users • Allow users to see specific individuals’ preferences • Aggregate user preferences into recommendations

15. Denizens of the small world • What is the small world like?

16. Denizens of the small world Passionate

17. Denizens of the small world Want community maintenance

18. Denizens of the small world Want recommendations

19. Why a new system? • We looked for an existing system • We found • Libraries (Taste, MultiLens, Suggest, …) • Web services (easyutil.com) • Research (no community-maintained recommenders) • Where are the off-the-shelf systems? • Hosted: Wikipedia, editme.com • Downloadable: Mediawiki

20. Not just beer WikiLens Asked about WikiLens: anime-planet.com frenchtowner.com course/teacher recs academic projects movielens users (for books) …

21. Outline • Motivation • Principles • System Design • Experiences • Possible Improvements

22. Let’s find a beer!

23. Principle: FIND • Beeradvocate.com has 32,000 beers • Anime planet has 1000s works of anime • FIND: Members should be able to find items that interest them • Information filtering is complex (Malone 1987) • cognitive (factual details) • economic (estimating cost/benefit) • social (friends, the crowd)

24. Let’s add a beer!

25. Principle: ADD • There’s a lot of interest in little-known items • “the market for books that are not even sold in the average bookstore is larger than the market for those that are.” (Anderson 2004)

26. “I wish this was sold in Montana” “You can’t get everything in NY” “are you people insane?”

27. Principle: ADD • There’s a lot of interest in little-known items • “the market for books that are not even sold in the average bookstore is larger than the market for those that are.” (Anderson 2004) • People work harder for immediate satisfaction • MovieLens members who saw their added movies immediately did more work than those who only saw their movies added after review. (Cosley 2005) • ADD: Members should be able to add items immediately

28. Principle: DEEP CHANGE • Our beer-lover wants a beer-centric system • Information common to each beer • Fields: style, brewer, alcohol content

29. Let’s add a beer field!

30. Principle: DEEP CHANGE • Our beer-lover wants a beer-centric system • Information common to each beer • Fields: style, brewer, alcohol content • Why not use a Content Management System? They support fields, but don’t support ADD • Power to the people: the community can do amazing things (Wikipedia) • DEEP CHANGE: Members should be able to uniquely identify items, and define and redefine their attributes and organization

31. Let’s rate a beer

32. Principle: MICRO-CONTRIBUTE • MovieLens users: rating is fun • 54% said it was a top 3 reason to rate • (Bryant and Forte): Small starter tasks may be a path for a casual contributor to become a more involved one • MICRO-CONTRIBUTE: Members should be able to make small contributions

33. Where are other beer lovers?

34. Principle: SEE OTHERS • “I’ll get by with a little help from my friends” • Every collaborative system should allow you to see other people (Erickson 2000) • social translucence (systems supporting visibility, awareness, and accountability) is a “fundamental requirement for supporting all types of communication and collaboration.” • SEE OTHERS: Members should be able to see each other and their contributions

35. Rebuilding beeradvocate? • Sure! Sort of, but .. • Other communities have the same needs • General (not just beer) • Anyone can start a new community • More power to the community: ADD, DEEP CHANGE • With a personalized recommender

36. Outline • Motivation • Principles • System Design • Experiences • Possible Improvements

37. Home page (FIND)

38. Beer category (FIND)

39. Predicted value of an item • Weighted average of buddy ratings and overall average rating • Not like traditional collaborative filtering • We believed in buddies • We thought traditional algorithms would be too noisy with little data

40. System Design (ADD) • An item is a wiki page

41. System Design (DEEP CHANGE) • A page is in a category (ex: “Beer”) • A category can have fields (ex: style)

42. System Design (DEEP CHANGE) • Fields have name, widget, options • Just another wiki page

43. System Design (DEEP CHANGE) • Users edit fields with familiar widgets

44. System Design (MICRO) • Ratings • Fields • Info • Comments

45. System Design (FIND) • Selecting: browsing, searching, filtering, ordering • Evaluating: item details, predictions, averages, buddy ratings, comments, page text

46. System Design (SEE OTHERS) • Buddies • On item pages • On category page (predictions, “likes”) • User pages (profiles and ratings) • Comments • Rating averages • Recent changes

47. System Design – wiki or not? • Wiki • Any user may edit items or categories • Data (including fields) is versioned • Recent changes • Not • Structured data fields with special editor • Ratings • Category with pages sorted by prediction

48. Outline • Motivation • Principles • System Design • Experiences • Possible Improvements

49. Experiences – wikilens.org stats • wikilens.org, April 2004 – Oct 2006 • 231 users • 4,430 items • 17,271 ratings

50. Experiences – wikilens.org cats