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눈으로 듣는 음악 추천 시스템

최규민(pi.314) / kakao corp.(추천팀)
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발표의 시작은? Music Streaming 서비스인 멜론과 카카오미니에 적용된
Toros 음악 추천 시스템의 다양한 추천 모델과 이를 활용한 추천 레시피에 대한 이야기를 합니다.

그리고 마무리는? 음악 추천을 위해 40~100차원 벡터로 모델링 된 (CB/CF) Latent Feature들을
다양한 조합으로 Visualization(t-SNE)하고 눈으로 탐색해 가면서 재미난 특징을 찾아보고자 합니다.
"CB와 CF Feature 노래를 어떻게 표현했을까?”,
"Popular노래와 Rare 한 노래의 Feature는?”
“Feedback이 많은 유저와 적은 유저 차이는?”
“팬덤이 있는 유저들은 어떤 모양일까?"

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눈으로 듣는 음악 추천 시스템

  1. 1. ~
  2. 2. ~
  3. 3. ~
  4. 4. ~ Matrix Factorization Word2Vec Deep Learning on audio
  5. 5. ~
  6. 6. .. Dance the night 1 Power up Forever young Viva la viva  Thinking out Loud  Counting Stars Something Just Like This
  7. 7. .. Dance the night 1 Power up Forever young Viva la viva  Thinking out Loud  Counting Stars Something Just Like This
  8. 8. .. Dance the night 1 Power up Forever young Viva la viva  Thinking out Loud  Counting Stars Something Just Like This
  9. 9. Push 1.5 5.6 User 18 DJ 3 / Song Vector .
  10. 10. 1.5 5.6 User 18 DJ 3 / Song Vector .
  11. 11. 1.5 5.6 User 18 DJ 3 / Song Vector . Feedback CF(Collaboration Filter) Contents CBF(Contents Based Filtering)
  12. 12. ?
  13. 13. ? …
  14. 14. ? filtering . … / 7080 X- 2000’ 2010’ 2018’ Top100 Rare - …..
  15. 15. 40D Dense Vector - Matrix Completion via ALS - User/Item Latent Feature Feedback Log(8day) - 4.3M(User) X 2.1M(Song) - 388M None zero values (Song)
  16. 16. 40D Dense Vector - Matrix Completion via ALS - User/Item Latent Feature Feedback Log(8day) - 4.3M(User) X 2.1M(Song) - 388M None zero values (Song) (Song) Vector Vector .
  17. 17. ? , .
  18. 18. Low level Feature Genre Classification Network
  19. 19. Low level Feature Genre Classification Network(Song) Vector Vector .
  20. 20. - 2004 12 1553
  21. 21. - 2004 12 1553 Stream + W2V
  22. 22. - 2004 12 1553 2004 12 Stream + W2V
  23. 23. - 2004 12 1553 Stream + MF 1553
  24. 24. Sampling 5M -> 16K Song Meta Mapping , , Stream Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D
  25. 25. Sampling 5M -> 16K Song Meta Mapping , , Stream Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D 5M Songs Top 1,000 Songs 15,000 Songs Uniform Sampling 1,000 Songs + = 16,000 Songs
  26. 26. Sampling 5M -> 16K Song Meta Mapping , , Stream Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D t-SNE
  27. 27. Sampling 5M -> 16K Song Meta Mapping , , Popularity Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D
  28. 28. Sampling 5M -> 16K Song Meta Mapping , , Popularity Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D
  29. 29. • Heavy • Top1000 Top1000 . • Popularity . • .
  30. 30. OST R&B CCM J-POP POP Global
  31. 31. Popularity
  32. 32. Light, Medium Song
  33. 33. Top1000 Top1000
  34. 34. • • MF • Top1000 Top1000 .
  35. 35. • • • Top1000 Top1000 .
  36. 36. 1,037 .
  37. 37. 45.2% 60.1% 59.9%
  38. 38. Stream Stream Stream
  39. 39. Stream Stream Stream 1. Streaming 1 2. Streaming 1 Streaming 97
  40. 40. Stream 1. 100 ~ 10,000 . 2. .
  41. 41. Song Vector Popular .
  42. 42. / / CCM /
  43. 43. .

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