File size: 2,250 Bytes
695b663 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ---
license: apache-2.0
task_categories:
- other
tags:
- recommendation
- ranking
- sequential-recommendation
---
# UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
This repository contains dataset artifacts for **UniRank**, an open benchmark for unified sequential modeling and feature interaction in large-scale recommendation ranking.
* **Paper:** [UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction](https://huggingface.co/papers/2607.19987)
* **GitHub Repository:** [salmon1802/UniRank](https://github.com/salmon1802/UniRank)
## Dataset Summary
UniRank standardizes chronological point-wise autoregressive supervision, multi-feedback evaluation, model implementations, data processing, and distributed training in one reproducible pipeline.
The benchmark evaluates unified ranking architectures on five large-scale industrial datasets from short-video, advertising, and e-commerce scenarios:
| Dataset | Instances | Users | Items | Fields | Tasks | Avg. length | Max. length |
|:--|--:|--:|--:|--:|--:|--:|--:|
| **QK-Video** | 493,306,303 | 4,996,176 | 3,752,235 | 10 | 4 | 99 | 6,013 |
| **KuaiRand** | 323,464,444 | 27,285 | 32,038,725 | 40 | 6 | 11,855 | 228,030 |
| **TAAC-25** | 757,207,146 | 7,706,778 | 15,707,425 | 30 | 2 | 98 | 100 |
| **Taobao** | 23,601,301 | 470,570 | 831,643 | 23 | 4 | 50 | 3,756 |
| **MerRec** | 172,304,959 | 1,697,072 | 42,577,610 | 20 | 5 | 102 | 26,576 |
## Download and Usage
The recommended workflow is to download a ready-to-use dataset from Hugging Face to local storage. For example, to download the KuaiRand dataset:
```bash
hf download salmon1802/KuaiRand \
--repo-type dataset \
--local-dir /path/to/data/KuaiRand_Video_Action
```
Please refer to the [GitHub Repository](https://github.com/salmon1802/UniRank) for preprocessing scripts, model configurations, and training instructions.
## Citation
If you find this dataset or benchmark useful, please cite the paper:
```bibtex
@article{unirank2026,
title={UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction},
author={UniRank Authors},
journal={arXiv preprint arXiv:2607.19987},
year={2026}
}
``` |