---
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}
}
```