See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction

• URL: http://arxiv.org/abs/2508.15217v1
• Date: Thu, 21 Aug 2025 04:05:31 GMT
• Title: See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction
• Authors: Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng, Lei Zhang, Sheng Chen, Chenghuan Hou, Han Zhu, Jian Xu, Bo Zheng,
• Abstract summary: Conversion rate (CVR) prediction is a core component of online advertising systems.<n>Traditional approaches restrict model training to labels from a single production-critical attribution mechanism.<n>We propose a novel Multi-Attribution Learning framework for CVR prediction that integrates signals from multiple attribution perspectives.
• Score: 30.31722186766052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-fundamentally determine label generation and model optimization. While many industrial platforms support diverse attribution mechanisms (e.g., First-Click, Last-Click, Linear, and Data-Driven Multi-Touch Attribution), conventional approaches restrict model training to labels from a single production-critical attribution mechanism, discarding complementary signals in alternative attribution perspectives. To address this limitation, we propose a novel Multi-Attribution Learning (MAL) framework for CVR prediction that integrates signals from multiple attribution perspectives to better capture the underlying patterns driving user conversions. Specifically, MAL is a joint learning framework consisting of two core components: the Attribution Knowledge Aggregator (AKA) and the Primary Target Predictor (PTP). AKA is implemented as a multi-task learner that integrates knowledge extracted from diverse attribution labels. PTP, in contrast, focuses on the task of generating well-calibrated conversion probabilities that align with the system-optimized attribution metric (e.g., CVR under the Last-Click attribution), ensuring direct compatibility with industrial deployment requirements. Additionally, we propose CAT, a novel training strategy that leverages the Cartesian product of all attribution label combinations to generate enriched supervision signals. This design substantially enhances the performance of the attribution knowledge aggregator. Empirical evaluations demonstrate the superiority of MAL over single-attribution learning baselines, achieving +0.51% GAUC improvement on offline metrics. Online experiments demonstrate that MAL achieved a +2.6% increase in ROI (Return on Investment).

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