Reinforcing User Retention in a Billion Scale Short Video Recommender System-Reference-Cited by-同舟云学术

Reinforcing User Retention in a Billion Scale Short Video Recommender System

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Companion Proceedings of the ACM Web Conference 2023
language:
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Author:

Cai Qingpeng¹^ORCID,Liu Shuchang¹^ORCID,Wang Xueliang¹^ORCID,Zuo Tianyou¹^ORCID,Xie Wentao¹^ORCID,Yang Bin¹^ORCID,Zheng Dong¹^ORCID,Jiang Peng¹^ORCID,Gai Kun²^ORCID

Affiliation:

1. Kuaishou Technology, China

2. Unaffiliated, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543873.3584640

Reference37 articles.

1. M Mehdi Afsar, Trafford Crump, and Behrouz Far. 2021. Reinforcement learning based recommender systems: A survey. arXiv preprint arXiv:2101.06286 (2021).

2. Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. 2018. Exploration by random network distillation. arXiv preprint arXiv:1810.12894 (2018).

3. Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, 2023. Two-Stage Constrained Actor-Critic fo Short Video Recommendation. arXiv preprint arXiv:2302.01680 (2023).

4. Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

5. Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation

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