User Retention: A Causal Approach with Triple Task Modeling

Zhang, Yang; Wang, Dong; Li, Qiang; Shen, Yue; Liu, Ziqi; Zeng, Xiaodong; Zhang, Zhiqiang; Gu, Jinjie; Wong, Derek F.

doi:10.24963/ijcai.2021/468

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

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3399-3405.

https://doi.org/10.24963/ijcai.2021/468

Keywords

Data Mining: Recommender Systems
Machine Learning Applications: Applications of Supervised Learning
Machine Learning: Deep Learning

Abstract

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.

Yang Zhang, Dong Wang, Qiang Li, Yue Shen, Ziqi Liu, Xiaodong Zeng, Zhiqiang Zhang, Jinjie Gu, and Derek F. Wong. 2021. User Retention: A Causal Approach with Triple Task Modeling. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3399–3405. https://doi.org/10.24963/ijcai.2021/468

@inproceedings{ijcai2021p468,
  title     = {User Retention: A Causal Approach with Triple Task Modeling},
  author    = {Zhang, Yang and Wang, Dong and Li, Qiang and Shen, Yue and Liu, Ziqi and Zeng, Xiaodong and Zhang, Zhiqiang and Gu, Jinjie and Wong, Derek F.},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {3399--3405},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/468},
  url       = {https://doi.org/10.24963/ijcai.2021/468},
}

TY  - CPAPER
TI  - User Retention: A Causal Approach with Triple Task Modeling
AU  - Zhang, Yang
AU  - Wang, Dong
AU  - Li, Qiang
AU  - Shen, Yue
AU  - Liu, Ziqi
AU  - Zeng, Xiaodong
AU  - Zhang, Zhiqiang
AU  - Gu, Jinjie
AU  - Wong, Derek F.
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 3399
EP  - 3405
DO  - 10.24963/ijcai.2021/468
UR  - https://doi.org/10.24963/ijcai.2021/468
ER  -