Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations

2025-07-20

AI Summary

In e-commerce recommendation, the “Click Item A, Buy Item B” (CABB) phenomenon introduces attribution bias in conversion modeling, violating the conventional click-conversion alignment assumption. Method: We propose a category-aware multi-task attribution modeling framework that (i) constructs a category similarity matrix to capture substitutional and complementary relationships among items, (ii) designs a collaborative filtering–based weighting mechanism to differentiate intra-category and cross-category conversion behaviors, and (iii) jointly optimizes click-through rate (CTR) and conversion rate (CVR) prediction via multi-task learning. Contribution/Results: The approach effectively mitigates model bias stemming from rigid click-conversion alignment. Offline evaluation shows a 13.9% reduction in normalized entropy; online A/B testing demonstrates a 0.25 percentage-point improvement in core conversion rate, validating both practical effectiveness and production deployability.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

User journeys in e-commerce routinely violate the one-to-one assumption that a clicked item on an advertising platform is the same item later purchased on the merchant's website/app. For a significant number of converting sessions on our platform, users click product A but buy product B -- the Click A, Buy B (CABB) phenomenon. Training recommendation models on raw click-conversion pairs therefore rewards items that merely correlate with purchases, leading to biased learning and sub-optimal conversion rates. We reframe conversion prediction as a multi-task problem with separate heads for Click A Buy A (CABA) and Click A Buy B (CABB). To isolate informative CABB conversions from unrelated CABB conversions, we introduce a taxonomy-aware collaborative filtering weighting scheme where each product is first mapped to a leaf node in a product taxonomy, and a category-to-category similarity matrix is learned from large-scale co-engagement logs. This weighting amplifies pairs that reflect genuine substitutable or complementary relations while down-weighting coincidental cross-category purchases. Offline evaluation on e-commerce sessions reduces normalized entropy by 13.9% versus a last-click attribution baseline. An online A/B test on live traffic shows +0.25% gains in the primary business metric.

Problem

• Addressing biased learning from Click A Buy B conversions
• Improving e-commerce recommendation models via multi-task prediction
• Enhancing conversion rates with taxonomy-aware collaborative filtering

Innovation

• Multi-task learning for CABA and CABB conversions
• Taxonomy-aware collaborative filtering weighting scheme
• Category-to-category similarity matrix from co-engagement logs