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pretty_name: EComAgentBench Product Database
language:
- en
tags:
- e-commerce
- llm-agents
- information-retrieval
---
# EComAgentBench Product Database
The prebuilt product and review database for **EComAgentBench**, accepted to **EMNLP 2026 Industry Track**.
[Paper](https://arxiv.org/abs/2606.17698) · [Code and benchmark](https://github.com/Morizeyao/EComAgentBench_)
This repository contains `product.db`, a SQLite database with FTS5 search indexes, covering approximately **3.7 million products** and **21.4 million reviews**. The file is **26.9GB**.
The **662 benchmark tasks**, agent code, and evaluation scripts are available in the [GitHub repository](https://github.com/Morizeyao/EComAgentBench_).
## Data source and use
The database is derived from [Amazon Reviews 2023](https://amazon-reviews-2023.github.io/) by McAuley Lab, using the `All_Beauty`, `Electronics`, `Cell_Phones_and_Accessories`, and `Office_Products` categories.
It is released with permission from the dataset authors for research evaluation. Use is subject to the original dataset's terms.
## Citation
If you use this database, please cite EComAgentBench and the underlying [Amazon Reviews 2023 dataset](https://amazon-reviews-2023.github.io/#citation).
```bibtex
@misc{du2026ecomagentbench,
title = {EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent},
author = {Zeyao Du and Tong Li and Haibo Zhang},
year = {2026},
eprint = {2606.17698},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2606.17698}
}
```
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