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Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

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Presented at the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017)

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Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

  1. 1. Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation Kosetsu Tsukuda and Masataka Goto National Institute of Advanced Industrial Science and Technology (AIST), Japan PAKDD 2017 2017/05/26
  2. 2. 2Play logs of online music services Online music service Play logs of Last.fm User ID Artist Song title Timestamp 55 Madonna Crazy For You 1/5/2017 11:24:35 141 The Beatles Let It Be 1/5/2017 11:28:16 55 Lady Gaga Poker Face 1/5/2017 11:28:30 โ‹ฎ
  3. 3. 3Userโ€™s song selection motivation ๐‘ก๐‘ก ๏ฌ By extracting a userโ€™s logs, his play history can be generated: it is observable when he listens to music and what he listens to ๏ฌ It is not observable why the user played each song I played this song because โ‹ฏ Research goal Given a userโ€™s play history, infer the userโ€™s song selection motivation of each played song
  4. 4. 4Factor 1: userโ€™s usual taste in music ๐‘ก๐‘ก ๏ฌ User has his/her own taste in music ๏ฌ Hypothesize that such taste affects useโ€™s song selection I played these songs because I like rock music. rockrockrock
  5. 5. 5Factor 2: userโ€™s addiction to an artist ๐‘ก๐‘ก ๏ฌ User can be addicted to an artist regardless of his taste in music (he watched the artist on TV or his friend recommended the artist) ๏ฌ Hypothesize that such addiction affects useโ€™s song selection My friend recommended an album of Lady Gaga. I played these songs because I became addicted to her. L.G. L.G.
  6. 6. 6What can our model do? ๐‘ก๐‘ก Taste Addiction Taste Taste: 73% Addiction: 27% Taste/Addiction ratio Taste Rock: 45% Hip-hop: 19% Addiction Lady Gaga: 45%Madonna: 32% Our model can estimate userโ€™s song selection motivation and userโ€™s statistics (e.g., taste in music and addicted artists)
  7. 7. 7What can our model do? Our model can reveal usersโ€™ addiction-based music listening behavior ๏ฌ Users have high addiction degree in the morning ๏ฌ Users have low addiction degree on the weekend ๐‘ก๐‘ก Taste Addiction Taste ๐‘ก๐‘ก Addiction Taste ๐‘ก๐‘ก Taste Addiction Addiction โ‹ฎ
  8. 8. Model
  9. 9. 9Overview ๏ฌ Estimate userโ€™s song selection motivation by using a topic model ๏ฌ Our model builds on the one proposed by Zheleve et al. in WWWโ€™10 that considers only userโ€™s taste in music Model by Zheleve et al. Taste Our model Taste Addiction+
  10. 10. 10Session ๐‘ก๐‘ก ๏ฌ Divide userโ€™s play logs into sessions ๏ฌ If the time gap between two songs is less than 30 min, they belong to the same session 41 min 135 min Session 1 Session 2 Session 3 ๐‘ก๐‘ก ๐‘ก๐‘ก
  11. 11. 11Song generative process by Zheleve et al. ๐‘ก๐‘ก ๏ฌ User has a topic distribution that represents the userโ€™s taste ๏ฌ One topic is assigned to each session based on the userโ€™s taste ๏ฌ Artist is selected based on the artist distribution of the topic Topic distribution = Userโ€™s taste Prob Topic 1 2 3 4 5 โ‹ฏ Topic 5 Artist distribution of topic 5 Prob โ‹ฏ Eminem JayZ J.Cole ICECUBE 2Pac Eminem J.Cole Eminem Topic 5 Topic 2 JayZ Annekei Annekei
  12. 12. 12Graphical model Topic distribution = Userโ€™s taste Prob Topic 1 2 3 4 5 โ‹ฏ Artist distribution of topic 5 Prob โ‹ฏ Eminem JayZ J.Cole ICECUBE 2Pac
  13. 13. 13Our Model ๏ฌ User has a taste-addiction weight that affects his song selection ๏ฌ In an addiction mode, the user directly chooses a song ๏ฌ User has an artist distribution that represents the userโ€™s addiction Prob Taste Addiction Taste-addiction weightAddicted artist distribution Prob โ‹ฏ LadyGaga DianaRoss BrunoMars Adele LMFAO
  14. 14. 14Song generative process in our model For each session ๏ฌ Choose a topic For each song in the session ๏ฌ Choose a mode (taste or addiction) ๏ฌ If taste mode, choose an artist according to the topic ๏ฌ Else if addiction mode, choose an artist from addiction artist distribution ๐‘ก๐‘ก Topic 5 Eminem LadyGaga LadyGaga Taste Addiction Addiction Addiction Taste Addicted artist distribution Prob โ‹ฏ LadyGaga DianaRoss BrunoMars Adele LMFAO Adele J.Cole
  15. 15. 15Graphical model Prob Taste Addiction Taste-addiction weightAddicted artist distribution Prob โ‹ฏ LadyGaga DianaRoss BrunoMars Adele LMFAO
  16. 16. Quantitative Experiments
  17. 17. 17Research Question Is adopting two factors, which are usersโ€™ taste in music and addiction to artists, effective to model music listening behavior? VS Model by Zheleve et al. Taste Our model Taste Addiction+
  18. 18. 18Dataset 1 ๏ฌ Play logs of a music download service in Japan ๏ฌ Consists of 10 weeks logs from 1/1/2016 to 10/3/2016 ๏ฌ 8 weeks are used as training data and 2 weeks as test data Users Artists Logs for train Sessions for train Logs for test Sessions for test 13,986 6,431 331,437 82,427 57,126 13,516 โ‹ฎ ๐‘ก๐‘ก ๐‘ก๐‘ก ๐‘ก๐‘ก 8 weeks: training 2 weeks: test 1/1/2016 25/2/2016 10/3/2016 13,986 users
  19. 19. 19Dataset 2 Users Artists Logs for train Sessions for train Logs for test Sessions for test 2,850 12,360 872,614 106,840 201,966 24,958 ๏ฌ Publicly available Play logs of Last.fm by Schedl in ICMRโ€™16 ๏ฌ Consists of 10 weeks logs from 1/1/2013 to 11/3/2013 ๏ฌ 8 weeks are used as training data and 2 weeks as test data โ‹ฎ ๐‘ก๐‘ก ๐‘ก๐‘ก ๐‘ก๐‘ก 8 weeks: training 2 weeks: test 1/1/2013 26/2/2013 11/3/2013 2,850 users
  20. 20. 20Settings Perplexity Number of topics ๐พ๐พ = 5, 10, 20, 30, 40, 50, 100, 200, and 300 ๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘๐‘ ๐ท๐ท๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = ๐‘’๐‘’๐‘’๐‘’๐‘’๐‘’ โˆ’ โˆ‘๐‘ข๐‘ขโˆˆ๐‘ˆ๐‘ˆ โˆ‘๐‘Ÿ๐‘Ÿ=1 ๐‘…๐‘…๐‘ข๐‘ข ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก โˆ‘๐‘—๐‘—=1 ๐‘‰๐‘‰๐‘ข๐‘ข๐‘ข๐‘ข ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก ๐‘๐‘ ๐‘Ž๐‘Ž๐‘ข๐‘ข๐‘ข๐‘ข๐‘ข๐‘ข โˆ‘๐‘ข๐‘ขโˆˆ๐‘ˆ๐‘ˆ โˆ‘๐‘Ÿ๐‘Ÿ=1 ๐‘…๐‘…๐‘ข๐‘ข ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก ๐‘‰๐‘‰๐‘ข๐‘ข๐‘ข๐‘ข ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก
  21. 21. 21Results Japanese service Perplexity Number of topics (K) 0 100 200 300 400 500 0 100 200 300 Zheleve Ours Number of topics (K) 500 800 1100 1400 1700 2000 2300 2600 0 100 200 300 Zheleve Ours Last.fm ๏ฌ Our model outperforms the model by Zheleve regardless of the number of topics in both datasets ๏ฌ We did not evaluate for ๐พ๐พ > 300 because if ๐‘ฒ๐‘ฒ becomes too large, many similar topics appear and it is difficult to carry out the qualitative evaluation
  22. 22. Qualitative Experiments
  23. 23. 23Data ๏ฌ Use 8 weeks logs of a Japanese service with ๐พ๐พ = 30 ๏ฌ Compute parameters for the evaluation 8 weeks logs of a Japanese service Parameters Usersโ€™ addiction ratio distribution Per-hour addiction ratio Per-topic addiction ratio ๐œƒ๐œƒ๐‘ข๐‘ข๐‘ข๐‘ข = ๐‘…๐‘…๐‘ข๐‘ข๐‘ข๐‘ข + ๐›ผ๐›ผ ๐‘…๐‘…๐‘ข๐‘ข + ๐›ผ๐›ผ๐พ๐พ ๐œ™๐œ™๐‘˜๐‘˜๐‘˜๐‘˜ = ๐‘๐‘๐‘˜๐‘˜๐‘˜๐‘˜ + ๐›ฝ๐›ฝ ๐‘๐‘๐‘˜๐‘˜ + ๐›ฝ๐›ฝ ๐ด๐ด ๐œ“๐œ“๐‘ข๐‘ข๐‘ข๐‘ข = ๐‘๐‘๐‘ข๐‘ข๐‘ข๐‘ข๐‘ข + ๐›พ๐›พ ๐‘๐‘๐‘ข๐‘ข๐‘ข + ๐›พ๐›พ ๐ด๐ด ๐œ†๐œ†๐‘ข๐‘ข๐‘ข = ๐‘๐‘๐‘ข๐‘ข๐‘ข + ๐œŒ๐œŒ ๐‘๐‘๐‘ข๐‘ข + 2๐œŒ๐œŒ ๐œ†๐œ†๐‘ข๐‘ข๐‘ข = ๐‘๐‘๐‘ข๐‘ข๐‘ข + ๐œŒ๐œŒ ๐‘๐‘๐‘ข๐‘ข + 2๐œŒ๐œŒ
  24. 24. 24Usersโ€™ addiction ratio distribution Is the addiction ratio different from one user to another?
  25. 25. 25Usersโ€™ addiction ratio distribution ๏ฌ Users with > 0.9 addiction ratio achieve the second highest peak ๏ฌ Many users are addicted to artists to a certain extent: this result further indicates the usefulness of considering the addiction mode Prob T A 0.72 Prob T A 0.11 Prob T A 0.53 โ‹ฎ 13,986 users 0 1500 3000 4500 6000 7500 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Frequencyofusers Addiction ratio T: taste A: addiction
  26. 26. 26Per-hour addiction ratio Is the addiction ratio different according to time?
  27. 27. 0.55 0.6 0.65 0.7 0.75 0 2 4 6 8 10 12 14 16 18 20 22 27Per-hour addiction ratio All usersโ€™ play logs between 6 AM and 7 AM ๐‘ƒ๐‘ƒ ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = 0.22 ๐‘ƒ๐‘ƒ ๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž = 0.78 ๐‘ƒ๐‘ƒ ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = 0.59 ๐‘ƒ๐‘ƒ ๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž = 0.41 ๐‘ƒ๐‘ƒ ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = 0.75 ๐‘ƒ๐‘ƒ ๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž = 0.25 โ‹ฎ Sum ๐‘๐‘(๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก) and ๐‘๐‘(๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž) of all logs respectively and normalize their sum to 1 Hour Addictionratio ๏ฌ Addiction ratio is high in the early morning and low at night ๏ฌ This knowledge can be applied to music recommendation (e.g., recommend unknown artists to the user at night) High addiction
  28. 28. 28Day of week based addiction ratio 0.6 0.61 0.62 0.63 0.64 0.65 Mon Tue Wed Thu Fri Sat Sun Addictionratio ๏ฌ Addiction ratio is high on weekdays and low on weekends ๏ฌ These results enable us to analyze peopleโ€™s music listening behavior from a new viewpoint Low addiction
  29. 29. 29Per-topic addiction ratio In which mode do users tend to listen to artistsโ€™ songs in each topic?
  30. 30. 30Per-topic addiction ratio All usersโ€™ play logs of top 20 artists in topic 19 Topic ID 0% 20% 40% 60% 80% 100% 10 8 19 20 1 4 26 12 21 30 2 9 13 18 11 6 14 25 5 24 15 3 22 27 23 16 29 7 28 17 addiction taste ๐‘ƒ๐‘ƒ ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = 0.66 ๐‘ƒ๐‘ƒ ๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž = 0.34 ๐‘ƒ๐‘ƒ ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = 0.52 ๐‘ƒ๐‘ƒ ๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž = 0.48 ๐‘ƒ๐‘ƒ ๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก๐‘ก = 0.74 ๐‘ƒ๐‘ƒ ๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž๐‘Ž = 0.26 โ‹ฎ ๏ฌ Topic 8 (low addiction) is related to anime songs and topic 28 (high addiction) is related to old Japanese artists ๏ฌ These results would be useful to generate a playlist
  31. 31. 31Summary ๏ผ’Carried our a quantitative evaluation Our model outperformed the one considering only userโ€™s taste in music ๏ผ“Carried out qualitative evaluations Usersโ€™ addiction ratio distribution, per-hour addiction ratio etc ๏ผ‘Proposed a new model for music listening behavior Our model considers userโ€™s usual taste in music and the addiction to artists Contributions Future work ๏ฌ Use the knowledge obtained from log analysis for artist recommendation and play list generation ๏ฌ Extend our model by considering the time transition of addiction

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