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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This document provides an overview and summary of the RecSys2018 conference that took place from October 2-7, 2018. It includes information about the keynote speakers, presented papers, and topics discussed. Some of the major topics covered include explainable recommendations, algorithmic confounding and homogeneity, offline evaluation of implicit feedback, calibrated recommendations, and using contextual bandits for artwork personalization at Netflix. The document also lists the dates and topics for each day of paper sessions and industry talks.

Engineering

AbemaTV
RecSys2018
2018 December 14th
CyberAgent, Inc. All Rights Reserved

RecSys Overview
•
• 12
• :
• :10/2-7
• (10/2)
• (10/3-5)
• (10/6-7)

•
• Long : 18% (181 Submissions)
• Short : 25% (150 Submissions)
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)

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

RecSys2019
https://recsys.acm.org/recsys19/
:2019 9/16-20
:
RecSys Challenge 2019 by Trivago

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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

2. 2018 6 AbemaTV

3. • RecSys • • Q&A

4. RecSys Overview

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

7. RecSys Overview

8. 800 (73% ) RecSys Overview

9. • 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)

12.

13. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility • feedback loop • • feedback loop https://arxiv.org/abs/1710.11214

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

16. Artwork Personalization at Netflix • Netflix Industry session • (ex ) • Contextial Bandit https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76 Artwork Personalization at Netﬂix

17. 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/

18. 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

19. 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”) •

20. 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

21.

22. RecSys2019 https://recsys.acm.org/recsys19/ :2019 9/16-20 : RecSys Challenge 2019 by Trivago

23. Thank you

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