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

TL;DR

ChorusCVR introduces a novel approach for CVR modeling that addresses sample selection bias and improves robustness by leveraging entire exposure space, including unclicked samples, in recommender systems.

Contribution

The paper proposes ChorusCVR, a new model that performs debiased CVR learning across the entire exposure space, effectively handling ambiguous negatives and factual negatives.

Findings

Reduces sample selection bias in CVR estimation.

Enhances robustness of CVR models against ambiguous negatives.

Achieves improved CVR prediction accuracy in experiments.

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.