| 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} | |
| } | |
| ``` | |