Tagommenders : Connecting Users to Items Through Tags

1. Tagommenders:Connecting Users to Items Through Tags Shilad Sen Macalester College Jesse Vig, John Riedl GroupLens Research

7. Tagommenders Analyze user interactions to infer liking (preferences) for tag concepts. Recommend items related to tag concepts liked by users.

9. Tagommender Goals • Recommend items using just tags. (Delicious) • Improve item recommendations with ratings by by using tags. (LibraryThing / Amazon) • accuracy • flexibility • explainability(Vig, IUI 2009).

10. Tagommender Flow Chart WALL-E animation robots pixar tag preference inference tag-based recommendation

11. MovieLens Tagging • Tagging introduced in 2006 • 15,000 distinct tags • 127,000 tag applications: • <user, tag, movie> • 4000 users applied >= 1 tag • 7700 movies with >= 1 tag app

12. Outline • Tag preference inference • Item recommendation • Auto-tagging and wrap-up

13. Outline • Tag preference inference • Item recommendation • Auto-tagging and wrap-up

14. Step 1: Tag Preference Inference ? animation robots pixar • Infer a user’s interest in tags from: • tags user applied • tags user searched for • user’s clicks on movie hyperlinks • user’s movie ratings

15. 118,017 ratings by 995 users

16. Preferences for Tags Searched / Applied

17. Movie-rating algorithm cars

18. Movie-Rating Algorithm cars 4 of 12 0.8 9 of 38 0.9 1 of 36 0.1

19. Generative Model: Expressive probabilistic processes. Model movie ratings. Separate model for every user, tag. Bayes-Rating Algorithm

20. Bayes-Rating Algorithm Jill’s Ratings for animated Movies N(μ=3.8,σ=0.7)

21. Bayes-Rating Algorithm WALL-E p(t| WALL-E) 1.0 - p(t| WALL-E) t = animation not t all possible normal dists for ratings for animated movies N(μu,σu) N(μu,t,σu,t) N(μ=2.0,σ=1.0) N(μ=4.0,σ=0.5)

22. Bayes-Rating Algorithm All movies m rated by Jill tagged with animation t = animation not t all possible normal dists for ratings for animated movies Toy Story WALL-E Shrek

24. Outline • Tag preference inference • Item recommendation • Auto-tagging and wrap-up

25. Tagommender Flow Chart WALL-E animation robots pixar tag preference inference tag-based recommendation

26. Standard machine learning problem With / without ratings Sixstandard recommender baselines Evaluate predictive performance Step #2: Tag-Based Recommendation

28. Outline • Tag preference inference • Item recommendation • Auto-tagging and wrap-up

29. Using Tag Preferences for Tag Inference

30. Top 10 Inferred Tags Not Already Applied

31. Tag preference inference: Systems can infer user preferences for tags. Item ratings help tag prefinference. Tag prefs can be used for auto-tagging. Tagommenders outperform traditional recommenders: Without ratings: moderate edge (10%). With ratings: slight edge (2%). Summary of Tagommenders

32. Future Work Alternative modalities for tags. Quality vs. preference. Thank You! GroupLens. MovieLens users. NSF grants IS 03-24851 and IIS 05-34420. Macalester College.

33. Shilad Sen ssen@macalester.edu (photo by flickr user SantiMB)