[2603.02184] MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms

Computer Science > Machine Learning arXiv:2603.02184 (cs) [Submitted on 2 Mar 2026] Title:MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms Authors:Jinqi Wu, Sishuo Chen, Zhangming Chan, Yong Bai, Lei Zhang, Sheng Chen, Chenghuan Hou, Xiang-Rong Sheng, Han Zhu, Jian Xu, Bo Zheng, Chaoyou Fu View a PDF of the paper titled MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms, by Jinqi Wu and 11 other authors View PDF HTML (experimental) Abstract:Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learning paradigm for conversion rate (CVR) prediction. However, the conversion labels in public CVR datasets are generated by a single attribution mechanism, hindering the development of MAL approaches. To address this data gap, we establish the Multi-Attribution Benchmark (MAC), the first public CVR dataset featuring labels from multiple attribution mechanisms. Besides, to promote reproducible research on MAL, we develop PyMAL, an open-source library covering a wide array of baseline methods. We conduct comprehensive experimental analyses on MAC and reveal three key insights: (1) MAL brings consistent performance gains across different attribution settings, especially for users featuring long conversion paths. (2) The performance growth scales up with objective complex...