Add dataset card, link to paper and GitHub repository

README.md

@@ -1,3 +1,63 @@
 ---
 license: apache-2.0
+task_categories:
+- other
+tags:
+- recommendation-system
+- sequential-recommendation
+- ranking
+- benchmark
 ---
+
+# UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
+
+[**GitHub**](https://github.com/salmon1802/UniRank) | [**Paper**](https://huggingface.co/papers/2607.19987)
+
+UniRank is an open benchmark for ranking models that unify sequential modeling and feature interaction in large-scale recommender systems. It standardizes chronological point-wise autoregressive supervision, multi-feedback evaluation, model implementations, and distributed training.
+
+This repository hosts preprocessed versions of the 5 industrial datasets used in the benchmark, spanning short-video, advertising, and e-commerce scenarios.
+
+## Dataset Overview
+
+The benchmark covers five large public datasets, with sequence lengths spanning from $10^2$ to $10^5$:
+
+| 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 |
+
+### Feedback Tasks
+
+* **QK-Video**: click, follow, like, share
+* **KuaiRand**: click, follow, like, comment, forward, long-view
+* **TAAC-25**: click, conversion
+* **Taobao**: click, cart, favorite, buy
+* **MerRec**: like, cart, offer, checkout, purchase
+
+## Quick Start
+
+You can download a ready-to-use dataset from the UniRank repositories using the Hugging Face CLI:
+
+```bash
+hf download salmon1802/KuaiRand \
+  --repo-type dataset \
+  --local-dir /path/to/data/KuaiRand_Video_Action
+```
+
+Refer to the [GitHub repository](https://github.com/salmon1802/UniRank) for dataset preprocessing scripts, benchmark configurations, and evaluation toolkits.
+
+## Citation
+
+If you find UniRank useful in your research, please cite the paper:
+
+```bibtex
+@article{unirank2026,
+  title={UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction},
+  author={Anonymous Authors},
+  journal={arXiv preprint arXiv:2607.19987},
+  year={2026}
+}
+```