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

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. 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 ⋮

3. 3User’s song selection motivation 𝑡𝑡  By extracting a user’s logs, his play history can be generated: it is observable when he listens to music and what he listens to  It is not observable why the user played each song I played this song because ⋯ Research goal Given a user’s play history, infer the user’s song selection motivation of each played song

4. 4Factor 1: user’s usual taste in music 𝑡𝑡  User has his/her own taste in music  Hypothesize that such taste affects use’s song selection I played these songs because I like rock music. rockrockrock

5. 5Factor 2: user’s addiction to an artist 𝑡𝑡  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)  Hypothesize that such addiction affects use’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. 6What can our model do? 𝑡𝑡 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 user’s song selection motivation and user’s statistics (e.g., taste in music and addicted artists)

7. 7What can our model do? Our model can reveal users’ addiction-based music listening behavior  Users have high addiction degree in the morning  Users have low addiction degree on the weekend 𝑡𝑡 Taste Addiction Taste 𝑡𝑡 Addiction Taste 𝑡𝑡 Taste Addiction Addiction ⋮

8. Model

9. 9Overview  Estimate user’s song selection motivation by using a topic model  Our model builds on the one proposed by Zheleve et al. in WWW’10 that considers only user’s taste in music Model by Zheleve et al. Taste Our model Taste Addiction+

10. 10Session 𝑡𝑡  Divide user’s play logs into sessions  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 𝑡𝑡 𝑡𝑡

11. 11Song generative process by Zheleve et al. 𝑡𝑡  User has a topic distribution that represents the user’s taste  One topic is assigned to each session based on the user’s taste  Artist is selected based on the artist distribution of the topic Topic distribution = User’s taste Prob Topic 1 2 3 4 5 ⋯ Topic 5 Artist distribution of topic 5 Prob ⋯ Eminem JayZ J.Cole ICECUBE 2Pac Eminem J.Cole Eminem Topic 5 Topic 2 JayZ Annekei Annekei

12. 12Graphical model Topic distribution = User’s taste Prob Topic 1 2 3 4 5 ⋯ Artist distribution of topic 5 Prob ⋯ Eminem JayZ J.Cole ICECUBE 2Pac

13. 13Our Model  User has a taste-addiction weight that affects his song selection  In an addiction mode, the user directly chooses a song  User has an artist distribution that represents the user’s addiction Prob Taste Addiction Taste-addiction weightAddicted artist distribution Prob ⋯ LadyGaga DianaRoss BrunoMars Adele LMFAO

14. 14Song generative process in our model For each session  Choose a topic For each song in the session  Choose a mode (taste or addiction)  If taste mode, choose an artist according to the topic  Else if addiction mode, choose an artist from addiction artist distribution 𝑡𝑡 Topic 5 Eminem LadyGaga LadyGaga Taste Addiction Addiction Addiction Taste Addicted artist distribution Prob ⋯ LadyGaga DianaRoss BrunoMars Adele LMFAO Adele J.Cole

15. 15Graphical model Prob Taste Addiction Taste-addiction weightAddicted artist distribution Prob ⋯ LadyGaga DianaRoss BrunoMars Adele LMFAO

16. Quantitative Experiments

17. 17Research Question Is adopting two factors, which are users’ 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. 18Dataset 1  Play logs of a music download service in Japan  Consists of 10 weeks logs from 1/1/2016 to 10/3/2016  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 ⋮ 𝑡𝑡 𝑡𝑡 𝑡𝑡 8 weeks: training 2 weeks: test 1/1/2016 25/2/2016 10/3/2016 13,986 users

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  Publicly available Play logs of Last.fm by Schedl in ICMR’16  Consists of 10 weeks logs from 1/1/2013 to 11/3/2013  8 weeks are used as training data and 2 weeks as test data ⋮ 𝑡𝑡 𝑡𝑡 𝑡𝑡 8 weeks: training 2 weeks: test 1/1/2013 26/2/2013 11/3/2013 2,850 users

20. 20Settings Perplexity Number of topics 𝐾𝐾 = 5, 10, 20, 30, 40, 50, 100, 200, and 300 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝐷𝐷𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑒𝑒𝑒𝑒𝑒𝑒 − ∑𝑢𝑢∈𝑈𝑈 ∑𝑟𝑟=1 𝑅𝑅𝑢𝑢 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ∑𝑗𝑗=1 𝑉𝑉𝑢𝑢𝑢𝑢 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑝𝑝 𝑎𝑎𝑢𝑢𝑢𝑢𝑢𝑢 ∑𝑢𝑢∈𝑈𝑈 ∑𝑟𝑟=1 𝑅𝑅𝑢𝑢 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑉𝑉𝑢𝑢𝑢𝑢 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡

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  Our model outperforms the model by Zheleve regardless of the number of topics in both datasets  We did not evaluate for 𝐾𝐾 > 300 because if 𝑲𝑲 becomes too large, many similar topics appear and it is difficult to carry out the qualitative evaluation

22. Qualitative Experiments

23. 23Data  Use 8 weeks logs of a Japanese service with 𝐾𝐾 = 30  Compute parameters for the evaluation 8 weeks logs of a Japanese service Parameters Users’ addiction ratio distribution Per-hour addiction ratio Per-topic addiction ratio 𝜃𝜃𝑢𝑢𝑢𝑢 = 𝑅𝑅𝑢𝑢𝑢𝑢 + 𝛼𝛼 𝑅𝑅𝑢𝑢 + 𝛼𝛼𝐾𝐾 𝜙𝜙𝑘𝑘𝑘𝑘 = 𝑁𝑁𝑘𝑘𝑘𝑘 + 𝛽𝛽 𝑁𝑁𝑘𝑘 + 𝛽𝛽 𝐴𝐴 𝜓𝜓𝑢𝑢𝑢𝑢 = 𝑁𝑁𝑢𝑢𝑢𝑢𝑢 + 𝛾𝛾 𝑁𝑁𝑢𝑢𝑢 + 𝛾𝛾 𝐴𝐴 𝜆𝜆𝑢𝑢𝑢 = 𝑁𝑁𝑢𝑢𝑢 + 𝜌𝜌 𝑁𝑁𝑢𝑢 + 2𝜌𝜌 𝜆𝜆𝑢𝑢𝑢 = 𝑁𝑁𝑢𝑢𝑢 + 𝜌𝜌 𝑁𝑁𝑢𝑢 + 2𝜌𝜌

24. 24Users’ addiction ratio distribution Is the addiction ratio different from one user to another?

25. 25Users’ addiction ratio distribution  Users with > 0.9 addiction ratio achieve the second highest peak  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 ⋮ 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. 26Per-hour addiction ratio Is the addiction ratio different according to time?

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 users’ play logs between 6 AM and 7 AM 𝑃𝑃 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 0.22 𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 0.78 𝑃𝑃 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 0.59 𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 0.41 𝑃𝑃 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 0.75 𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 0.25 ⋮ Sum 𝑝𝑝(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡) and 𝑝𝑝(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎) of all logs respectively and normalize their sum to 1 Hour Addictionratio  Addiction ratio is high in the early morning and low at night  This knowledge can be applied to music recommendation (e.g., recommend unknown artists to the user at night) High addiction

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  Addiction ratio is high on weekdays and low on weekends  These results enable us to analyze people’s music listening behavior from a new viewpoint Low addiction

29. 29Per-topic addiction ratio In which mode do users tend to listen to artists’ songs in each topic?

30. 30Per-topic addiction ratio All users’ 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 𝑃𝑃 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 0.66 𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 0.34 𝑃𝑃 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 0.52 𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 0.48 𝑃𝑃 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 0.74 𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 0.26 ⋮  Topic 8 (low addiction) is related to anime songs and topic 28 (high addiction) is related to old Japanese artists  These results would be useful to generate a playlist

31. 31Summary ２Carried our a quantitative evaluation Our model outperformed the one considering only user’s taste in music ３Carried out qualitative evaluations Users’ addiction ratio distribution, per-hour addiction ratio etc １Proposed a new model for music listening behavior Our model considers user’s usual taste in music and the addiction to artists Contributions Future work  Use the knowledge obtained from log analysis for artist recommendation and play list generation  Extend our model by considering the time transition of addiction

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

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

Recommended

More Related Content

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

Similar to Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation(13)

More from Kosetsu Tsukuda

More from Kosetsu Tsukuda(20)

Recently uploaded

Recently uploaded(20)

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