The Benefit of Using Tag-Based Profiles LA-Web 2007

1. The Benefit of UsingTag-Based Profiles LA-Web 2007 Claudiu S. Firan Wolfgang Nejdl Raluca Paiu L3S Research Center 2008/03/14

2. Introduction • Collaborative tagging has emerged as an important way to organize, provide and share information about the resources on the Web. • Recent research has shown that such tag distributions stabilize over time, and can be used to improve search on the Web. • How can they be used to enable personalized recommendations? • More specifically : Music recommendations.

3. Current System • Collaborative Filtering • Cold start problem • Poor variety • Content Similarity • Similarity does not imply preference • Hybrid Methods • Complex for general users • Tags not used for recommendation

4. Related Work • Most music recommender systems are based on collaborative filtering • Other approaches exist • FOAF : friend-of-a-friend, RSS • { users, ratings, contents } Bayesian network • But, • Profiles not automatically inferred from music data • Profiles are track based, not tag based • Similar approach • Bookmarking recommendation on Del.icio.us

5. Functionalities & Usage Data ( Last.fm ) • Track • User • Tag

6. Track Data ( Last.fm ) • Track name • Artist name • Album name • Tags & score • Number of times has been played • User comments

7. Track Data ( Last.fm )

8. Track Data ( Last.fm )

9. Track Data ( Last.fm )

10. Track Data ( Last.fm )

11. Track Data ( Last.fm )

12. Track Data ( Last.fm )

13. Track Data ( Last.fm ) • Total 317,058 tracks

14. Tag Data ( Last.fm ) • Number of times has been used • Number of users have used • Similar tags with scores

15. Tag Data ( Last.fm )

16. Tag Data ( Last.fm ) • Total 21,177 tags

17. Tag Data ( Last.fm )

18. User Data ( Last.fm ) • ID • Gender • Age • Location • Register date • Number of tracks • Friends, Neighbors, Groups • Tags

19. User Data ( Last.fm )

20. User Data ( Last.fm ) • Total 289,654 users • Filter : • > 50 tracks • > 10 tags • 12,193 users left

21. User Profiles • Track-based • A list of < track, score > pairs. • Tag-based • A list of < tag, score > pairs.

22. User Profiles

23. Music Recommendations • Collaborative Filtering based on Tracks • baseline • Collaborative Filtering based on Tags • CFTTI, CFTTN, CFTG • Search based on Tags • STTI, STTN, STG

24. Music Recommendations

25. Music Recommendations track tag all user user similar user recommend track Lucene similarity

26. Music Recommendations track tag all user R user similar user recommend track R CFTR Lucene similarity

27. Music Recommendations track tag CFTTI, CFTTN all user R G R user similar user recommend track G CFTG G Lucene similarity R

28. Music Recommendations track tag STTI, STTN all user R G R user recommend track G STG Lucene similarity R

29. Evaluation • 7variant algorithms each returns 10 recommends • 18 subject to rate • Rating • Preference : 0, 1, 2 • Novelty : 0, 1, 2 • NDCG, Popularity, Novelty

30. Results - 1

31. Results - 2

32. Results - 3

33. Conclusions • Analyze tag usage of the most popular music community site, Last.fm • Compare user profiles based on tags with conventional based on tracks • Specify recommendation algorithms based on tag-based user profiles