Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction
This work addresses persistent issues in recommender systems for improving conversion rate estimation, representing an incremental advancement over existing methods.
The paper tackled the challenges of sample selection bias and data sparsity in post-click conversion rate prediction for recommender systems by proposing an entire-space variational information exploitation framework, which achieved a 2.25% average improvement over state-of-the-art baselines on six large-scale datasets.
In recommender systems, post-click conversion rate (CVR) estimation is an essential task to model user preferences for items and estimate the value of recommendations. Sample selection bias (SSB) and data sparsity (DS) are two persistent challenges for post-click conversion rate (CVR) estimation. Currently, entire-space approaches that exploit unclicked samples through knowledge distillation are promising to mitigate SSB and DS simultaneously. Existing methods use non-conversion, conversion, or adaptive conversion predictors to generate pseudo labels for unclicked samples. However, they fail to consider the unbiasedness and information limitations of these pseudo labels. Motivated by such analysis, we propose an entire-space variational information exploitation framework (EVI) for CVR prediction. First, EVI uses a conditional entire-space CVR teacher to generate unbiased pseudo labels. Then, it applies variational information exploitation and logit distillation to transfer non-click space information to the target CVR estimator. We conduct extensive offline experiments on six large-scale datasets. EVI demonstrated a 2.25\% average improvement compared to the state-of-the-art baselines.