Reinforcing User Retention in a Billion Scale Short Video Recommender System

Qingpeng Cai,Shuchang Liu,Xueliang Wang, Tianyou Zuo,Wentao Xie, Bin Yang, Dong Zheng,Peng Jiang, Kun Gai

COMPANION OF THE WORLD WIDE WEB CONFERENCE, WWW 2023(2023)

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摘要
Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users). Retention is a long-term feedback after multiple interactions of users and the system, and it is hard to decompose retention reward to each item or a list of items. Thus traditional point-wise and list-wise models are not able to optimize retention. In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance. We formulate the problem as an infnite-horizon request-based Markov Decision Process, and our objective is to minimize the accumulated time interval of multiple sessions, which is equal to improving the app open frequency and user retention. However, current reinforcement learning algorithms can not be directly applied in this setting due to uncertainty, bias, and long delay time incurred by the properties of user retention. We propose a novel method, dubbed RLUR, to address the afore-mentioned challenges. Both ofine and live experiments show that RLUR can signifcantly improve user retention. RLUR has been fully launched in Kuaishou app for a long time, and achieves consistent performance improvement on user retention and DAU.
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关键词
short video,user retention,reinforcement learning
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