Using tags and topic models to describe and compare playlists
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Playlists are a natural delivery method for music recom- mendation and discovery systems. Recommender systems offering playlists must strive to make them relevant and enjoyable. In this paper we survey many current means of generating and evaluating playlists. We present a means of comparing playlists in a reduced dimensional space through the use of aggregated tag clouds and topic models. To evaluate the fitness of this measure, we perform prototypical retrieval tasks on playlists taken from radio station logs gathered from Radio Paradise and Yes.com, using tags from Last.fm with the result showing better than random performance when using the query playlist’s station as ground truth, while fail- ing to do so when using time of day as ground truth. We then discuss possible applications for this measurement technique as well as ways it might be improved. Presented at the Workshop on Music Recommendation and Discovery on 26 September 2010, co-located with ACM Recommender Systems.
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Using tags and topic models to describe and compare playlists
- 1. Using Song Social Tags and Topic Models to Describe and Compare Playlists Ben Fields Christhophe Mark b.fields@gold.ac.uk Rhodes d’Inverno Sunday, September 26, 2010
- 2. overview – motivation – describing playlists – comparing playlists – use and evaluation 2 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 3. motivation 3 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 4. how is music consumed? 4 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 5. listening. 5 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 6. is music recommendation broken? 6 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 7. listening. 7 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 8. perhaps we should consider recommendations in the context of their playorder 8 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 9. perhaps we should consider recommendations in playlists 9 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 10. describing playlists 10 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 11. describing playlists song representation Artist: dev/null Title : Zombie Sunset 11 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 12. describing playlists song representation Artist: dev/null Title : Zombie Sunset 11 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 13. describing playlists song representation 11 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 14. describing playlists song representation 11 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 15. describing playlists song reduction tag weight breakcore 100 idm 60 electronic 35 => P (Ti ) experimental 35 grindcore 10 ... ... 12 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 16. describing playlists topic models – a tag cloud is a representation of a song – a topic is a pdf of all tags – many weighted topics can model tag clouds – dimensionality is the number of topics 13 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 17. describing playlists Latent Direchlete Allocation ! # " z w N M 14 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 18. describing playlists Latent Direchlete Allocation gather tags for all songs create LDA model describing topic distributions infer topic mixtures for all songs create vector database of playlists 15 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 19. comparing playlists 16 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 20. comparing playlists sequence matching 17 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 21. comparing playlists sequence matching 17 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 22. comparing playlists sequence matching 17 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 23. comparing playlists sequence matching 17 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 24. comparing playlists sequence matching 17 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 25. comparing playlists distance between sequences – Compatible with any n-dimensional distance measure – In our evaluation we use multi- dimensional euclidean distance – For query and retrieval among playlists we use audioDB 18 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 26. evaluation 19 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 27. evaluation test sets – Radio Paradise –18 months of logs from 1 station –partitioned with marked links – yes.com –1 week of logs from 9 genres of stations, eval uses ‘rock’ and ‘jazz’ –partitioned every hour 20 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 28. evaluation test sets source St Smt Pt Pavg(time) Pavg(songs) whole yes.com 885810 2543 70190 55min 12.62 “Rock” stations 105952 865 9414 53min 11.25 “Jazz” stations 36593 1092 3787 55min 9.66 “Radio Paradise” 195691 2246 45284 16min 4.32 stics for both the radio log datasets. Symbols are as follows: St is t n the dataset; Smt is the total number of songs in St where tags could lists; Pavg(time) is the average runtime ofWOMRAD 2010 21 Fields et al. - Tags, Topics and Playlists these playlists and Pavg(songs) i . Sunday, September 26, 2010
- 29. evaluation finding dayparts using both datasets, will playlists retrieve to others that are played around the same time? 22 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 30. evaluation finding dayparts 23 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 31. evaluation finding dayparts 24 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 32. evaluation retrieval by station is a playlist similar to others from its own station? 25 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 33. evaluation retrieval by station 26 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 34. evaluation retrieval by station 27 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 35. evaluation retrieval by station 28 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 36. conclusions summary – tag clouds and topic models for representation of playlists – sequence matching – two evaluations –dayparting needed better data –station based queries showed solid result 29 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 37. conclusions future work –other distance metrics –more datasets –better labels –alignment with humans 30 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 38. resources – audioDB http://omras2.doc.gold.ac.uk/software/audiodb – gensim http://nlp.fi.muni.cz/projekty/gensim/ – slides: http://slideshare.com/BenFields – contact: b.fields@gold.ac.uk http://blog.benfields.net twitter: @alsothings 31 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010
- 39. resources – audioDB http://omras2.doc.gold.ac.uk/software/audiodb – gensim http://nlp.fi.muni.cz/projekty/gensim/ – slides: http://slideshare.com/BenFields – contact: b.fields@gold.ac.uk http://blog.benfields.net twitter: @alsothings Questions? 31 Fields et al. - Tags, Topics and Playlists WOMRAD 2010 Sunday, September 26, 2010