1. The Benefit of Using Tag-Based Profiles Claudiu Firan, Wolfgang Nejdl, Raluca Paiu 5 th Latin American Web Congress, 2007

2. Music Recommendation 2 Collaborative Filtering Personal Music Community Data

3. Challenges Collaborative Filtering Content Based Techniques Hybrid Methods Cold start problem Items with no ratings Users with no profile Poor artist variety in recommended pieces Slow Unreliability in modeling user’s preferences Content similarity does not necessarily reflect preferences Slow Heavy user input 3

4. New Approach 4 Collaborative Filtering Search Personal Music Community Data Personal Tags Collaborative Filtering

5. Why Use Tags? Tags are: Written chaotically Not verified Unstructured Heterogeneous Unreliable But if many, the correct ones arise “Wisdom of the masses” 5

6. Last.fm – “The Social Music Revolution” 6 Track Artist Similar Artists Albums Track Usage Info Similar Tracks Tags (with weight) User Comments

7. Tracks, Tags, and Profiles 7

8. User Profiles weight=preference(user,item) 8 … … …

9. Track-based Profiles (TR) preference(user,track) = log(user_track_#listened) 9 TR …

10. Track-Tag-based Profiles (TT) preference(user,tag) = log( Σ i ( log(user_track i _#listened) ∙ log(user_tag_track i _#tagged))) [∙ ITF(tag)] ITF = Inverse Tag Frequency With: TTI Without: TTN 10 TTN TTI …

11. Tag-based Profiles (TG) preference(user,tag) = log(user_tag_#used) 11 TG …

12. User Profiles from Personal MP3s 1.Read personal playlist from PC 2.Match MP3s against our database 3.Add overall average usage information values 12

13. Collaborative Filtering vs. Search 13

14. Track- & Tag-based Recommendations 14 Collaborative Filtering …

15. Tag-based Search 15 …

16. Algorithms 16 Collaborative Filtering Search Tracks (baseline) CF-TR Track-Tag ITF CF-TT-IS-TT-I Track-Tag NoITF CF-TT-NS-TT-N Tag CF-TGS-TG

17. Experiments & Outcome 17

18. Last.fm Crawled Data 317,058 tracks 21,177 tags (most prominent ones are music genres) 289,654 users  12,193 listened at least 50 tracks and used at least 10 tags 18

19. Experimental Setup 1.Create user profiles 18 subjects 658 tracks on average in user profile (not statistically significant in influencing algorithm outcome) 2.Run algorithms 7 algorithms 10 recommended items per algorithm per user 3.Two scores Quality of recommendation [0-2]  NDCG Novelty of recommendation [0-2]  Average 19

20. Results 20 NrAlgorithmNDCGSignif. vs. CFTR Average Novelty Average Popularity 1CFTR0.54-1.3915,177 2CFTG0.25Highly1.834,065 3CFTTI0.36Highly1.726,632 4CFTTN0.37Highly1.7413,671 5STG0.60No1.077,587 6STTI0.73Highly0.8210,380 7STTN0.77Highly0.7816,309 CFTR: Baseline STG: Lower popularity Higher quality STG: Lower popularity Higher quality STTI & STTN: Huge improvement Statistically significant STTI & STTN: Huge improvement Statistically significant NDCG – Novelty: High inverse correlation Pearson c = -0.987 NDCG – Novelty: High inverse correlation Pearson c = -0.987

21. Gain over the Baseline (CF on Tracks) 21

22. Conclusions CF on tag-based profiles worse than CF on track-based profiles Search with tags improved recommendation performance substantially 44% increase in quality Instant results – virtually no time delay No cold start problem Tag-based profiles work also with less rich music repositories Results probably influenced by the consistent tag usage on Last.fm: mostly genres 22