| --- |
| 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} |
| } |
| ``` |
