Improve dataset card: add paper link, GitHub repository and evaluation usage

Hi, I'm Niels from the Hugging Face team.

This PR improves the dataset card for the UserMirrorer evaluation set. The changes include:
- Adding a link to the research paper: [Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation](https://huggingface.co/papers/2508.18142).
- Adding a link to the official GitHub repository.
- Adding relevant tags for better discoverability.
- Adding a sample usage section for evaluation based on the instructions in the GitHub README.
- Adding the BibTeX citation for the paper.

README.md

@@ -1,4 +1,12 @@
 ---
+language:
+- en
+license: cc-by-sa-4.0
+task_categories:
+- text-generation
+tags:
+- recommendation-system
+- user-simulation
 dataset_info:
   features:
   - name: text
@@ -34,25 +42,46 @@ configs:
   data_files:
   - split: test
     path: data/test-*
-license: cc-by-sa-4.0
-task_categories:
-- text-generation
-language:
-- en
 ---
 
+# UserMirrorer-eval
 
-# UserMirrorrer-eval
-This is the evaluation set of UserMirrorer.
+This is the evaluation set of **UserMirrorer**, presented in the paper [Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation](https://huggingface.co/papers/2508.18142).
 
-Please refer to our paper: "Mirroring Users: Towards Building Preference-aligned User Simulator with Recommendation Feedback".
+**Code**: [Joinn99/UserMirrorer](https://github.com/Joinn99/UserMirrorer)
 
 ## Notice
 
-In the `UserMirrorer` dataset, the raw data from `MIND` and `MovieLens-1M` datasets are distributed under restrictive licenses and cannot
-be included directly.
+In the `UserMirrorer` dataset, the raw data from `MIND` and `MovieLens-1M` datasets are distributed under restrictive licenses and cannot be included directly.
+
+Therefore, we provide a comprehensive, step-by-step pipeline to load the original archives, execute all necessary preprocessing operations, and assemble the final UserMirrorer training and test splits.
+
+Click [here](https://colab.research.google.com/github/UserMirrorer/UserMirrorer/blob/main/UserMirrorer_GetFullDataset.ipynb) to run the script notebook on Google Colab to get the full dataset. Also, you can download it and run it locally.
+
+## Evaluation Usage
+
+To run the evaluation, you can execute the following command provided in the official repository:
+
+```bash
+python usermirrorer/run_eval.py \
+    --project_path <YOUR_WORKING_DIR> \     # The path to your working directory
+    --model_path <MODEL_PATH> \             # The path to the model
+    --input_file <INPUT_FILE> \             # The path to the input file
+    --output_file <OUTPUT_FILE> \           # The path to the output file
+    --mode <MODE> \                         # The mode of the evaluation
+    --repeat_times <REPEAT_TIMES> \         # The number of sampling times
+```
 
-Therefore, we provide a comprehensive, step-by-step pipeline to load the original archives, execute all necessary preprocessing
-operations, and assemble the final UserMirrorer training and test splits.
+## Citation
 
-Click [here](https://colab.research.google.com/github/UserMirrorer/UserMirrorer/blob/main/UserMirrorer_GetFullDataset.ipynb) to run the script notebook on Google Colab to get the full dataset. Also, you can download it and run it locally.
+```bibtex
+@misc{wei2025mirroringusersbuildingpreferencealigned,
+      title={Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation}, 
+      author={Tianjun Wei and Huizhong Guo and Yingpeng Du and Zhu Sun and Huang Chen and Dongxia Wang and Jie Zhang},
+      year={2025},
+      eprint={2508.18142},
+      archivePrefix={arXiv},
+      primaryClass={cs.HC},
+      url={https://arxiv.org/abs/2508.18142}, 
+}
+```