Add dataset card and link to paper

README.md

@@ -0,0 +1,47 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+- text-generation
+tags:
+- recommendation-system
+- preference-optimization
+- dpo
+---
+
+# DynamicPO-Data
+
+This repository contains the processed datasets for the paper [DynamicPO: Dynamic Preference Optimization for Recommendation](https://huggingface.co/papers/2605.00327).
+
+**Official Code**: [xingyuHuxingyu/DynamicPO](https://github.com/xingyuHuxingyu/DynamicPO)
+
+## Dataset Summary
+DynamicPO is a plug-and-play dynamic preference optimization framework for LLM-based recommender systems. This repository provides the processed data used to evaluate the framework, following the construction pipeline of prior works like [LLaRA](https://arxiv.org/abs/2312.02445) and [S-DPO](https://arxiv.org/abs/2406.09215).
+
+The collection includes processed splits for:
+- **LastFM**: Music recommendation data.
+- **Goodreads**: Book recommendation data.
+- **Steam**: Game recommendation data.
+
+The data consists of processed splits for supervised fine-tuning (SFT), preference optimization (PO), and evaluation.
+
+## Data Preparation
+As noted in the official GitHub repository, the LastFM data can be extracted using the following command:
+
+```bash
+cd ./data
+unzip lastfm-sft-cans20.zip
+```
+
+The processed splits for Goodreads and Steam are also available within this dataset release.
+
+## Citation
+If you find this work useful, please consider citing the following paper:
+
+```bibtex
+@article{hu2026dynamicpo,
+  title={DynamicPO: Dynamic Preference Optimization for Recommendation},
+  author={Hu, Xingyu and Zhang, Kai and Wu, Jiancan and Wang, Shuli and Wang, Chi and Chen, Wenshuai and Zhu, Yinhua and Wang, Haitao and Wang, Xingxing and Wang, Xiang},
+  journal={arXiv preprint arXiv:2605.00327},
+  year={2026}
+}
+```