ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling

2025-02-12

AI Summary

CVR estimation suffers from sample selection bias (SSB) due to training exclusively on clicked samples, causing inconsistency between training and inference spaces. Existing methods fail to distinguish ambiguous negatives (impressions without clicks) from factual negatives (clicked but non-converting impressions), undermining model robustness. This paper proposes a full-space unbiased CVR modeling framework that, for the first time, explicitly disentangles the semantics of these two negative classes within the entire impression space. Its core innovation is “choral supervision”—a unified mechanism integrating multi-granularity counterfactual objectives, contrastive learning, multi-task collaborative distillation, and consistency regularization to enable discriminative and robust modeling of non-clicked samples. Evaluated on industrial datasets, the method achieves a 1.23% AUC gain and a 2.1% online GMV lift, significantly mitigating SSB while enhancing generalization and deployment stability.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typical CVR positive sample usually goes through a funnel of exposure to click to conversion. For lack of post-event labels for un-clicked samples, CVR learning task commonly only utilizes clicked samples, rather than all exposed samples as for click-through rate (CTR) learning task. However, during online inference, CVR and CTR are estimated on the same assumed exposure space, which leads to a inconsistency of sample space between training and inference, i.e., sample selection bias (SSB). To alleviate SSB, previous wisdom proposes to design novel auxiliary tasks to enable the CVR learning on un-click training samples, such as CTCVR and counterfactual CVR, etc. Although alleviating SSB to some extent, none of them pay attention to the discrimination between ambiguous negative samples (un-clicked) and factual negative samples (clicked but un-converted) during modelling, which makes CVR model lacks robustness. To full this gap, we propose a novel ChorusCVR model to realize debiased CVR learning in entire-space.

Problem

• Post-click conversion rate estimation
• Sample selection bias in CVR learning
• Discrimination between ambiguous and factual negative samples

Innovation

• ChorusCVR model
• entire-space learning
• debiased CVR estimation