DCMT: a direct entire-space causal multi-task framework for post-click conversion estimation

Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Yu Lu, Tiehua Zhang, Jun Zhou*, Chaochao Chen, Fei Wu, Guanfeng Liu, Yan Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)

Abstract

In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task Learning (MTL) frameworks (Category 1) or causal debiasing frameworks (Category 2) to incorporate more auxiliary data in the entire exposure/inference space D or debias the selection bias in the click/training space O. However, these two kinds of solutions cannot effectively address the not-missing-at-random problem and debias the selection bias in O to fit the inference in D. To fill the research gaps, we propose a Direct entire-space Causal Multi-Task framework, namely DCMT, for post-click conversion prediction in this paper. Specifically, inspired by users' decision process of conversion, we propose a new counterfactual mechanism to debias the selection bias in D, which can predict the factual CVR and the counterfactual CVR under the soft constraint of a counterfactual prior knowledge. Extensive experiments demonstrate that our DCMT can improve the state-of-the-art methods by an average of 1.07% in term of CVR AUC on the offline datasets and 0.75% in term of PV-CVR on the online A/B test (the Alipay Search). Such improvements can increase millions of conversions per week in real industrial applications, e.g., the Alipay Search.

Original languageEnglish
Title of host publication2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3113-3125
Number of pages13
ISBN (Electronic)9798350322279
ISBN (Print)9798350322286
DOIs
Publication statusPublished - 2023
Event39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023

Publication series

Name
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Conference

Conference39th IEEE International Conference on Data Engineering, ICDE 2023
Country/TerritoryUnited States
CityAnaheim
Period3/04/237/04/23

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