解决电商推荐中点击与购买不一致的问题，提升转化率。
Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations
- 将转化预测拆分为点击买同款和点击买不同款两类任务
- 通过品类相似度加权，区分真实替代/互补关系与偶然购买
- 线上测试转化率提升0.25%，效果优于传统点击归因
电商用户行为常违背点击与购买一一对应假设：大量转化会话中，用户点击商品A却购买商品B，即‘点击A，购买B’（CABB）现象普遍存在。基于原始点击-转化对训练推荐模型会奖励仅相关联的商品，导致学习偏差、转化率下降。本文将转化预测重构为多任务问题，分别建模点击买同款（CABA）与点击买不同款（CABB）。为区分有效与无效的CABB转化，提出一种基于产品分类体系的协同过滤加权方法：将每个商品映射到分类树叶节点，从大规模共互动日志中学习品类间相似度矩阵，强化具有真实替代或互补关系的配对，弱化跨品类偶然购买。离线评估显示，归一化熵降低13.9%；在线A/B测试在真实流量中带来主业务指标+0.25%提升。
原文摘要 · Abstract (English)
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.
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