# Reinforcing User Retention in a Billion Scale Short Video Recommender System

**Authors:** Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, Kun Gai  
**arXiv:** [2302.01724](https://arxiv.org/abs/2302.01724)  
**Date:** 2023-02-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2302.01724v1/

## Key points

- Authors address the challenge of optimizing user retention on short video platforms
- Traditional models struggle to effectively optimize retention due to its long-term nature and complexity
- Proposed novel method called RLUR based on reinforcement learning techniques
- Formulated problem as an infinite-horizon request-based Markov Decision Process with objective of minimizing time interval between user sessions
- RLUR shows promising results in both offline and live experiments, implemented in Kuaishou app
- Method consistently improves user retention and DAU metrics

## Summary

In their paper titled "Reinforcing User Retention in a Billion Scale Short Video Recommender System," authors Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, and Kun Gai address the challenge of optimizing user retention on short video platforms. These platforms have experienced rapid growth by recommending engaging content to users with the aim of increasing Daily Active Users (DAU). However, traditional models struggle to effectively optimize retention due to its long-term nature and complexity. To tackle this issue, the authors propose a novel method called RLUR based on reinforcement learning techniques. The authors formulate the problem as an infinite-horizon request-based Markov Decision Process with the objective of minimizing the time interval between multiple user sessions to improve app open frequency and user retention. Despite challenges such as uncertainty and bias in current reinforcement learning algorithms caused by user retention properties, RLUR shows promising results in both offline and live experiments. The method has been successfully implemented in the Kuaishou app and consistently improves user retention and DAU metrics. Overall, this research contributes valuable insights into enhancing user retention strategies on short video platforms through innovative reinforcement learning approaches.