User Retention: A Causal Approach with Triple Task Modeling
User Retention: A Causal Approach with Triple Task Modeling
Yang Zhang, Dong Wang, Qiang Li, Yue Shen, Ziqi Liu, Xiaodong Zeng, Zhiqiang Zhang, Jinjie Gu, Derek F. Wong
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 3399-3405.
https://doi.org/10.24963/ijcai.2021/468
For many Internet companies, it has been an important focus to improve user retention rate. To achieve this goal, we need to recommend proper services in order to meet the demands of users. Unlike conventional click-through rate (CTR) estimation, there are lots of noise in the collected data when modeling retention, caused by two major issues: 1) implicit impression-revisit effect: users could revisit the APP even if they do not explicitly interact with the recommender system; 2) selection bias: recommender system suffers from selection bias caused by user's self-selection. To address the above challenges, we propose a novel method named UR-IPW (User Retention Modeling with Inverse Propensity Weighting), which 1) makes full use of both explicit and implicit interactions in the observed data. 2) models revisit rate estimation from a causal perspective accounting for the selection bias problem. The experiments on both offline and online environments from different scenarios demonstrate the superiority of UR-IPW over previous methods. To the best of our knowledge, this is the first work to model user retention by estimating the revisit rate from a causal perspective.
Keywords:
Machine Learning: Deep Learning
Machine Learning Applications: Applications of Supervised Learning
Data Mining: Recommender Systems