Add paper link, code link, and metadata

README.md

@@ -1,4 +1,45 @@
 ---
 license: apache-2.0
+task_categories:
+- other
+tags:
+- advertising
+- conversion-rate-prediction
+- multi-attribution-learning
 ---
-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.
+
+# 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}, 
+}
+```