Add dataset card, link to paper and GitHub

README.md

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+---
+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}
+}
+```