ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866915151861514240 |
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| author | Cheng, Wei Lu, Yucheng Xia, Boyang Cao, Jiangxia Xu, Kuan Wen, Mingxing Jiang, Wei Zhang, Jiaming Liu, Zhaojie Hong, Liyin Gai, Kun Zhou, Guorui |
| author_facet | Cheng, Wei Lu, Yucheng Xia, Boyang Cao, Jiangxia Xu, Kuan Wen, Mingxing Jiang, Wei Zhang, Jiaming Liu, Zhaojie Hong, Liyin Gai, Kun Zhou, Guorui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling Cheng, Wei Lu, Yucheng Xia, Boyang Cao, Jiangxia Xu, Kuan Wen, Mingxing Jiang, Wei Zhang, Jiaming Liu, Zhaojie Hong, Liyin Gai, Kun Zhou, Guorui Information Retrieval Social and Information Networks 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. |
| title | ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling |
| topic | Information Retrieval Social and Information Networks |
| url | https://arxiv.org/abs/2502.08277 |