2. Evaluation of cultural similarity in playlist generation Mariusz Kleć Polish-Japanese Institute of Information Technology in Warsaw, Poland http://mariuszklec.pjwstk.edu.pl

3. Schedule of my presentation 1. Introduction 2. Playlists 3. Cultural similarity 4. Playlist Generator software 5. Experiment 6. Results 7. Questions / discussion

4. Introduction Large music collections

5. Playlists definition

6. Playlists Automatic playlist generation user initial action playlist generation Query-by-example

7. Playlists playlists created by query-by-example method

8. …how often particular artists co-occur on the same web pages… Cultural similarity artist similarity

9. Cultural similarity Scoring functions D. P.W. Ellis, B. Whitman, A. Berenzweig, S. Lawrence. The quest for ground truth in musical artist similarity. Proceedings of the International Conference on Music Information Retrieval. 170–7, 2002 Geleijnse, G., and J. Korst. Web-based artist categorization. Proceedings of the International Conference on Music Information Retrieval. 266–71, 2006

10. Playlist Generator software Author’s software

11. Playlist Generator software similarity matrix

12. Playlist Generator software making playlist using similarity matrix Higher similarity Lower similarity Queen 2 Diana Krall 3 Cranberries 4 Aerosmith 5 Bon Jovi 6 Rod Steward 7 Bach 8 The Rolling Stones 9 Tina Turner 10 Phil Collins1

13. Experiment What’s the goal?

14. Experiment details A playlist

15. Experiment details

16. Results Software and people’s playlist similarities

17. Results Average value of similarities between playlists

18. Results summary Automatic playlists generation process that incorporates f2 function is more effective as regards people’s expectation about artists similarity

19. Questions