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AbemaTV
RecSys2018
2018 December 14th
CyberAgent, Inc. All Rights Reserved
2018 6
AbemaTV
• RecSys
•
• Q&A
RecSys Overview
RecSys Overview
•
• 12
• :
• :10/2-7
• (10/2)
• (10/3-5)
• (10/6-7)
•
• Long : 18% (181 Submissions)
• Short : 25% (150 Submissions)
RecSys Overview
RecSys Overview
800 (73% )
RecSys Overview
•
RecSys Overview
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
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)
AbemaTV レコメンド開発エンジニアによる RecSys 2018 参加レポート
How Algorithmic Confounding in Recommendation Systems
Increases Homogeneity and Decreases Utility
• feedback loop
•
• feedback loop
https://arxiv.org/abs/1710.11214
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
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
Artwork Personalization at Netflix
• Netflix Industry session
•
(ex
)
• Contextial Bandit
https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76
Artwork Personalization at Netflix
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/
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
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”)
•
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
AbemaTV レコメンド開発エンジニアによる RecSys 2018 参加レポート
RecSys2019
https://recsys.acm.org/recsys19/
:2019 9/16-20
:
RecSys Challenge 2019 by Trivago
Thank you

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  • 5. RecSys Overview • • 12 • : • :10/2-7 • (10/2) • (10/3-5) • (10/6-7)
  • 6. • • Long : 18% (181 Submissions) • Short : 25% (150 Submissions) RecSys Overview
  • 8. 800 (73% ) RecSys Overview
  • 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. 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)
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  • 14. 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
  • 15. 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
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