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metadata 
dataset_info:
  - config_name: absolute_and_random_price
    features:
      - name: query
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      - name: experiment_label
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      - name: rating_count
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      - name: price
        sequence: float64
      - name: overall_pick
        sequence: bool
      - name: best_seller
        sequence: bool
      - name: limited_time
        sequence: bool
      - name: discounted
        sequence: bool
      - name: low_stock
        sequence: bool
      - name: stock_quantity
        sequence: int64
      - name: position_in_experiment
        sequence: int64
      - name: assigned_position
        sequence: int64
      - name: desired_choice
        sequence: int64
      - name: original_price
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      - name: screenshot
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      - name: assigned_position
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      - name: desired_choice
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      - name: brand
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      - name: color
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      - name: budget
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        dtype: image
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  - config_name: relative_price
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        dtype: string
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configs:
  - config_name: absolute_and_random_price
    data_files:
      - split: data
        path: absolute_and_random_price/data-*
  - config_name: instruction_following
    data_files:
      - split: data
        path: instruction_following/data-*
  - config_name: rating
    data_files:
      - split: data
        path: rating/data-*
  - config_name: relative_price
    data_files:
      - split: data
        path: relative_price/data-*
license: mit

ACE-RS Dataset

Dataset Description

ACE-RS (Agentic e-CommercE - Rationality Suite) is a comprehensive dataset for studying how autonomous AI agents behave when shopping in e-commerce settings. The dataset enables controlled experiments on AI shopping behavior by providing synthetic tasks which evaluate agents' economic rationality and basic instruction following.

Paper

📄 Paper: What is your AI Agent buying? Evaluation, Implications and Emerging Questions for Agentic e-Commerce

Dataset Subsets

The ACE-RS dataset contains multiple experimental subsets:

1. Instruction Following

  • instruction_following:
    • Budget-constrained shopping tasks
    • Color-based product selection
    • Brand-specific requirements

2. Rationality Tests

  • absolute_and_random_price
  • relative_price
  • rating

Usage

For detailed instructions on how to use this dataset and run experiments, please refer to the ACES codebase.

Citation

If you use the ACERS dataset in your research, please cite:

@misc{allouah2025acers,
    author       = { Amine Allouah, Omar Besbes, Josué D Figueroa, Yash Kanoria and Akshit Kumar },
    title        = { ACE-RS (Revision bb94051) },
    year         = 2025,
    url          = { https://huggingface.co/datasets/My-Custom-AI/ACE-SR },
    doi          = { 10.57967/hf/6123 },
    publisher    = { Hugging Face }
}

License

This dataset is licensed under the MIT License and includes data publically available from Amazon.com

Links