docs: add concise dataset card

README.md

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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}
+}
+```