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Recommenders Everywhere: The WikiLens Community-Maintained Recommender System

Recommenders Everywhere: The WikiLens Community-Maintained Recommender System

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Recommenders Everywhere: The WikiLens Community-Maintained Recommender System

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  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

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

  3. WikiLens

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

  5. Some People Love Sharing

  6. Some People Love Sharing

  7. Some People Love Sharing

  8. Some People Love Sharing YouTube craigslist eBay …

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

  10. 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

  11. 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. 

  12. 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

  13. 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

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

  15. Denizens of the small world Passionate

  16. Denizens of the small world Want community maintenance

  17. Denizens of the small world Want recommendations

  18. 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

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

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

  21. Let’s find a beer!

  22. 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)

  23. Let’s add a beer!

  24. 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)

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

  26. 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

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

  28. Let’s add a beer field!

  29. 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

  30. Let’s rate a beer

  31. 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

  32. Where are other beer lovers?

  33. 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

  34. 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

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

  36. Home page (FIND)

  37. Beer category (FIND)

  38. 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

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

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

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

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

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

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

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

  46. 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

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

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

  49. Experiences – wikilens.org cats