| --- |
| license: apache-2.0 |
| task_categories: |
| - other |
| tags: |
| - advertising |
| - conversion-rate-prediction |
| - multi-attribution-learning |
| --- |
| |
| # MAC: Multi-Attribution BenChmark |
|
|
| [Paper](https://huggingface.co/papers/2603.02184) | [Code](https://github.com/alimama-tech/PyMAL) |
|
|
| Conversion Rate (CVR) prediction is a cornerstone of online advertising systems. However, existing public CVR datasets—such as Criteo and Ali-CCP—provide conversion labels derived from a single attribution mechanism, severely limiting research into more holistic modeling paradigms. To bridge this gap, we introduce **MAC (Multi-Attribution BenChmark)**, the first public CVR dataset featuring labels from multiple attribution mechanisms. |
|
|
| ## Overview |
| MAC is the first benchmark featuring labels from multiple attribution mechanisms, specifically designed to foster research in Multi-Attribution Learning (MAL). By learning from conversion labels yielded by multiple attribution mechanisms, models can obtain a more comprehensive and robust understanding of touchpoint value. |
|
|
| Along with the dataset, the authors provide **PyMAL**, an open-source library covering a wide array of baseline methods (such as MMoE, PLE, and MoAE) for industrial-scale CVR prediction. |
|
|
| ## Dataset Structure |
| The files in this repository are organized as follows: |
| - `train/`: Training data files. |
| - `test/`: Test data files. |
| - `vocabs/`: ID mappings and vocabulary files for features. |
|
|
| ## Usage |
| To download the dataset directly via `git clone`: |
| ```bash |
| git clone https://huggingface.co/datasets/alimamaTech/MAC data |
| ``` |
|
|
| ## Citation |
| ```bibtex |
| @misc{wu2026macconversionrateprediction, |
| title={MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms}, |
| author={Jinqi Wu and Sishuo Chen and Zhangming Chan and Bird Bai and Lei Zhang and Sheng Chen and Chenghuan Hou and Xiang-Rong Sheng and Han Zhu and Jian Xu and Bo Zheng and Chaoyou Fu}, |
| year={2026}, |
| eprint={2603.02184}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2603.02184}, |
| } |
| ``` |
