---
language:
- en
license: cc-by-sa-4.0
task_categories:
- text-generation
tags:
- recommendation-system
- user-simulation
dataset_info:
  features:
  - name: text
    struct:
    - name: action_list
      sequence: string
    - name: history
      dtype: string
    - name: profile
      dtype: string
  - name: user_id
    dtype: string
  - name: item_id
    dtype: string
  - name: timestamp
    dtype: timestamp[ns]
  - name: item_pos
    dtype: int64
  - name: choice_cnt
    dtype: int64
  - name: dataset
    dtype: string
  - name: impression_list
    sequence: string
  splits:
  - name: test
    num_bytes: 14699116
    num_examples: 6400
  download_size: 5555631
  dataset_size: 14699116
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# UserMirrorer-eval

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).

**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.

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
```

## Citation

```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}, 
}
```