ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation

• URL: http://arxiv.org/abs/2204.05125v1
• Date: Sun, 3 Apr 2022 08:12:27 GMT
• Title: ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
• Authors: Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, Wei Chu
• Abstract summary: Methods in Entire Space Multi-task Model (ESMM) family leverage sequential pattern of user actions to address data sparsity issue. ESMM suffers from Inherent Estimation Bias (IEB) and Potential Independence Priority (PIP) issues. We devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM$2$), which employs a counterfactual risk miminizer as a regularizer.
• Score: 14.346868328637115
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, i.e. $impression\rightarrow click \rightarrow conversion$ to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM$^2$), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM$^2$ can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.

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