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.

Code: 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 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:

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

@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}, 
}