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Video Recommender in Viki (VikiでのVideoレコメンド事例)

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Here is slide material in Rakuten Technology Conference "前夜祭".

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Video Recommender in Viki (VikiでのVideoレコメンド事例)

  1. 1. Video Recommender in Viki Co-work with Viki developers Takashi Umeda (梅田卓志) @umekoumeda
  2. 2. 2 Do you know Viki ?
  3. 3. 3 Click!!
  4. 4. 4 Free Video Streaming service
  5. 5. 5 + Anime, TV Drama & Movie
  6. 6. 6 + Subtitle
  7. 7. 7 Existing recommender Select videos randomly from videos in same country & genre Click Rate : 0.09%
  8. 8. 8 Objective Boost CTR of recommender in video page Click Rate : 0.09%
  9. 9. 9 User behavior + Content attributes
  10. 10. 10 User behavior + Content attributes
  11. 11. 11 Goal Video A Video B Select videos for each video Video Video Video Video Video Video Recommend Recommend : :
  12. 12. 12 Recommendation by users’ behavior Select videos watched by common users TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 TVNW: TvN Genre: Romance Year: 2013 User 1 User 2 User 3 Watch Watch Watch Watch Watch video A video B video C
  13. 13. 13 Recommendation by users’ behavior Select videos watched by common users TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 TVNW: TvN Genre: Romance Year: 2013 User 1 User 2 User 3 Watch Watch Watch Watch Watch video A video B video C
  14. 14. 14 Recommendation by users’ behavior Recommend “B” on page “A” TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 video A video B Recommend
  15. 15. 15 But,...
  16. 16. 16 Issue Ratio of videos having results : 42% Popular Videos Video Minor Videos No results 42% 58% 100% 80% 60% 40% 20% 0%
  17. 17. 17 User behavior Content attributes + Episode / Parts Other attributes
  18. 18. 18 User behavior Content attributes + Episode / Parts Other attributes
  19. 19. 130,976 videos 79,601 videos 19 Parts Merge parts into one video Jungle Emperor Leo Part1 Jungle Emperor Leo Part2 Jungle Emperor Leo
  20. 20. 79,601 videos 22,844 videos 20 Episodes Merge episodes into one video Doctor X Episode 1 Doctor X Episode 2 Doctor X
  21. 21. 75% 21 Issue Ratio of videos having results : 42% Popular Videos Video Video Minor Videos No results 75% 25% 100% 80% 60% 40% 20% 0%
  22. 22. 22 Issue Remaining 25%  How should we do ? Popular Videos Video Video Minor Videos No results 75% 25% 100% 80% 60% 40% 20% 0%
  23. 23. 23 User behavior Content attributes + Episode / Parts Other attributes
  24. 24. 24 Procedure Which attributes are same ? TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 TVNW: TvN Genre: Romance Year: 2013 User 1 User 2 User 3 Watch Watch Watch Watch Watch video A video B video C
  25. 25. 25 Probability of videos having same attributes TV NW > Country > Genre > Actor, .. TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 TVNW: TvN Genre: Romance Year: 2013 User 1 User 2 User 3 Watch Watch Watch Watch Watch video A video B video C
  26. 26. 26 Application Select videos focusing on TV NW TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 video A video B video D TVNW: SBS Genre: Romance Year: 2014 Rec. Minor videos (25%)
  27. 27. 27 Application Select videos focusing on TV NW TVNW: SBS Genre: Romance Year: 2013 TVNW: SBS Genre: Action Year: 2014 video A video B video D TVNW: SBS Genre: Romance Year: 2014 Rec. Same TVNW’s videos Minor videos (25%)
  28. 28. 28 0.13 0.12 0.11 0.1 0.09 0.08 Old New Click Rate [%] AB test Click Rate : +32.4% It uses AB test frame work ‘Turing’ developed by Ishan
  29. 29. It rolled out across the world 29 New recommender DC DC DC DC Front-end is developed by Huy & Yan Han
  30. 30. 30 Click on the web !
  31. 31. Download Mobile App! 31
  32. 32. 32 Appendix
  33. 33. 33 Contents-based recommendation #1 Fix weights of attribute by user-behavior rec. result Similarity(video A, video B) = w1 * Genre Similarity (genre of video A, genre of video B) w2 * Country Similarity (country of video A, country of video B) w3 * Actor Similarity (actors in video A, actors in video B) : • If attributes are matched, it’s 1. • Otherwise, it’s 0. Weights are fixed by user-behavior rec. result
  34. 34. 34 Contents-based recommendation #2 • Fix weigh by using user behavior recommeder result • Estimate similarity for videos which have no results 1.Training 2.Test Video A Video B Genre A Genre B Jaccard similarity 1v 2v kpop rock 99.0 1v 3v kpop jazz 3.1 1v 4v kpop classic 2.1 Video A Video B Genre A Genre B Jaccard similarity 5v 2v jpop rock ? 5v 3v jpop jazz ? 5v 4v kpop classic ? Fix weight Similarity (videoA, videoB ; W) Estimate jaccard similarity Similarity (videoA, videoB ; W)

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