xingyuHuxingyu/DynamicPO-Data
DynamicPO-Data This repository contains the processed datasets for the paper DynamicPO: Dynamic Preference Optimization for Recommendation. Official Code: 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 and S-DPO. The collection… See the full description on the dataset page: https://huggingface.co/datasets/xingyuHuxingyu/DynamicPO-Data.
DynamicPO-Data
This repository contains the processed datasets for the paper DynamicPO: Dynamic Preference Optimization for Recommendation.
Official Code: 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 and S-DPO.
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:
cd ./data
unzip lastfm-sft-cans20.zipThe 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:
@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}
}