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AbemaTV レコメンド開発エンジニアによる RecSys 2018 参加レポート

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AbemaTV レコメンド開発エンジニアによる RecSys 2018 参加レポート

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AbemaTV レコメンド開発エンジニアによる RecSys 2018 参加レポート

  1. 1. AbemaTV RecSys2018 2018 December 14th CyberAgent, Inc. All Rights Reserved
  2. 2. 2018 6 AbemaTV
  3. 3. • RecSys • • Q&A
  4. 4. RecSys Overview
  5. 5. RecSys Overview • • 12 • : • :10/2-7 • (10/2) • (10/3-5) • (10/6-7)
  6. 6. • • Long : 18% (181 Submissions) • Short : 25% (150 Submissions) RecSys Overview
  7. 7. RecSys Overview
  8. 8. 800 (73% ) RecSys Overview
  9. 9. • RecSys Overview
  10. 10. 10/3 10/4 10/5 Keynote1 Keynote2 Paper Session 5: RecSys that Care Paper Session 1: Explanations Paper Session 3: Learning & Optimazation Paper Session 6: Metrics & Evaluations Industry Session 1: Alogrithms Industry Session 2: System Considerations Paper Session 7: Beyond Users & Items Paper Session 2: Products Paper Session 4: Travel & Entertainment Keynote3 RecSys Overview
  11. 11. KeyNote Five E’s: Reflecting on the Design of Recommendations Elizabeth F. Churchill (Google) • Explainable (understandable/intelligible) • Equitable(fair & impartial) • Ethical(morally good or correct) • Expedient (convenient & practical) • Exigent (pressing & demanding)
  12. 12. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility • feedback loop • • feedback loop https://arxiv.org/abs/1710.11214
  13. 13. Unbiased Offline Recommender Evaluation for Missing-Not-At-Random Implicit Feedback • • Inverse Propensity Score • Causal Embeddings for Recommendation • Criteo AI Labs RecSys 2018 Best long paper • • Domain Adaptatin
  14. 14. Calibrated Recommendations • Netflix • 70% 30% • User KL divergence (Calibration Metric) • Calibration Metric • Divercity Black : play history Red : before calibration Green : after calibration https://dl.acm.org/citation.cfm?id=3240372
  15. 15. Artwork Personalization at Netflix • Netflix Industry session • (ex ) • Contextial Bandit https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76 Artwork Personalization at Netflix
  16. 16. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits • Sportify Contextual Bandits • Contextual Bandit BART • music https://hellogiggles.com/reviews-coverage/music/spotify-playlist-favorite-songs-of-2017-wrapped/
  17. 17. GENERATION MEETS RECOMMENDATION: Proposing Novel Items for Groups of Users • • item • VAE Encoder Decoder Z item z • item k Z • z Decoder item( feature) https://haroldsoh.files.wordpress.com/2018/10/sohvo_recsys18.pdf
  18. 18. Interpreting User Inaction in Recommender Systems • item MovieLens • 7 ("Would Not Enjoy” “Watched” “Not Noticed” “Not Now” “ Others Better” “Explore Later or Decided To Watch”) •
  19. 19. A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant? • CTR • MovieLens • Judging Similarity: A User-Centric Study of Related Item Recommendations • MovieLens 6 • CF
  20. 20. RecSys2019 https://recsys.acm.org/recsys19/ :2019 9/16-20 : RecSys Challenge 2019 by Trivago
  21. 21. Thank you

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