Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations
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arXiv
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| Format: | Preprint |
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2025
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| author | Zeng, Xiangyu Jaspal, Amit Liu, Bin Panneeru, Goutham Huang, Kevin Bievre, Nicolas Jaggi, Mohit Maniraju, Prathap Jain, Ankur |
| author_facet | Zeng, Xiangyu Jaspal, Amit Liu, Bin Panneeru, Goutham Huang, Kevin Bievre, Nicolas Jaggi, Mohit Maniraju, Prathap Jain, Ankur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations Zeng, Xiangyu Jaspal, Amit Liu, Bin Panneeru, Goutham Huang, Kevin Bievre, Nicolas Jaggi, Mohit Maniraju, Prathap Jain, Ankur Information Retrieval 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. |
| title | Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2507.15113 |