--- dataset_info: - config_name: absolute_and_random_price features: - name: query dtype: string - name: experiment_label dtype: string - name: experiment_number dtype: int64 - name: id sequence: string - name: title sequence: string - name: url sequence: string - name: image_url sequence: string - name: sponsored sequence: bool - name: rating sequence: float64 - name: rating_count sequence: int64 - 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 sequence: float64 - name: screenshot dtype: image splits: - name: data num_bytes: 343809718 num_examples: 2500 download_size: 221069168 dataset_size: 343809718 - config_name: instruction_following features: - name: query dtype: string - name: experiment_label dtype: string - name: experiment_number dtype: int64 - name: id sequence: string - name: title sequence: string - name: url sequence: string - name: image_url sequence: string - name: sponsored sequence: bool - name: rating sequence: float64 - name: rating_count sequence: int64 - 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: brand sequence: string - name: color sequence: string - name: budget sequence: float64 - name: cheapest sequence: bool - name: screenshot dtype: image splits: - name: data num_bytes: 132899376 num_examples: 400 download_size: 130748969 dataset_size: 132899376 - config_name: rating features: - name: query dtype: string - name: experiment_label dtype: string - name: experiment_number dtype: int64 - name: id sequence: string - name: title sequence: string - name: url sequence: string - name: image_url sequence: string - name: sponsored sequence: bool - name: rating sequence: float64 - name: rating_count sequence: int64 - 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 sequence: float64 - name: screenshot dtype: image splits: - name: data num_bytes: 224375820.5 num_examples: 1500 download_size: 155227111 dataset_size: 224375820.5 - config_name: relative_price features: - name: query dtype: string - name: experiment_label dtype: string - name: experiment_number dtype: int64 - name: id sequence: string - name: title sequence: string - name: url sequence: string - name: image_url sequence: string - name: sponsored sequence: bool - name: rating sequence: float64 - name: rating_count sequence: int64 - 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 sequence: float64 - name: screenshot dtype: image splits: - name: data num_bytes: 218926260.5 num_examples: 1500 download_size: 36822808 dataset_size: 218926260.5 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](https://arxiv.org/abs/2508.02630) ## 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](https://github.com/mycustomai/ACES). ## Citation If you use the ACERS dataset in your research, please cite: ```bibtex @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](LICENSE) and includes data publically available from Amazon.com ## Links - 💻 **Code Repository**: [https://github.com/mycustomai/ACES](https://github.com/mycustomai/ACES) - 📊 **Dataset**: [HuggingFace Dataset Card](https://huggingface.co/datasets/My-Custom-AI/ACE-RS) - 📄 **Paper** [Arxiv](https://arxiv.org/abs/2508.02630)