[2210.11039] Entire Space Counterfactual Learning for Reliable Content Recommendations

Computer Science > Machine Learning arXiv:2210.11039 (cs) [Submitted on 20 Oct 2022 (v1), last revised 25 Mar 2026 (this version, v3)] Title:Entire Space Counterfactual Learning for Reliable Content Recommendations Authors:Hao Wang, Zhichao Chen, Zhaoran Liu, Haozhe Li, Degui Yang, Xinggao Liu, Haoxuan Li View a PDF of the paper titled Entire Space Counterfactual Learning for Reliable Content Recommendations, by Hao Wang and 6 other authors View PDF HTML (experimental) Abstract:Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure $\rightarrow$ click $\rightarrow$ conversion, constructing surrogate learning tasks for CVR estimation. However, these methods suffer from two significant defects: (1) intrinsic estimation bias (IEB), where the CVR estimates are higher than the actual values; (2) false independence prior (FIP), where the causal relationship between clicks and subsequent conversions is potentially overlooked. To overcome these limitations, we develop a model-agnostic framework, namely Entire Space Counterfactual Multitask Model (ESCM$^2$), which incorporates a counterfactual risk minimizer within the ESMM framework to regularize CVR estimation. Experiments conducted on large-scale industrial recommendat...