---
license: apache-2.0
task_categories:
- text-generation
- feature-extraction
language:
- en
tags:
- e-commerce
- products
- amazon
- recommendations
size_categories:
- 1K<n<10K
dataset_info:
  config_name: test_cases
  features:
  - name: id
    dtype: int64
  - name: name
    dtype: string
  - name: query
    dtype: string
  - name: expected_tools
    dtype: string
  - name: expected_category
    dtype: string
  - name: success_criteria
    dtype: string
  - name: difficulty
    dtype: string
  - name: notes
    dtype: string
  splits:
  - name: train
    num_bytes: 2306
    num_examples: 10
  download_size: 5551
  dataset_size: 2306
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
- config_name: test_cases
  data_files:
  - split: train
    path: test_cases/train-*
---

# Amazon Products Sample Dataset

A curated sample of 2,000 popular products from the Amazon Reviews 2023 dataset, designed for educational use in building RAG (Retrieval-Augmented Generation) systems and shopping agents.

## Dataset Description

This dataset contains product metadata across 4 categories:
- **Electronics** (500 products)
- **Video Games** (500 products)
- **Books** (500 products)
- **Home & Kitchen** (500 products)

Products were filtered to include only those with 500+ reviews, ensuring recognizable, well-documented items.

## Dataset Structure

Each product includes:
- `parent_asin`: Unique product identifier
- `title`: Product title
- `description`: Product description (list of strings)
- `price`: Listed price (string)
- `price_numeric`: Price as float
- `average_rating`: Average star rating (1-5)
- `rating_number`: Number of reviews
- `features`: Product features (list)
- `categories`: Category hierarchy (list)
- `category`: Simplified category label
- `store`: Store/brand name
- `images`: Product images (list of dicts)
- `main_category`: Original main category

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("gatech-scheller-ai-in-business/amazon-products")
df = dataset['train'].to_pandas()

# Filter by category
electronics = df[df['category'] == 'Electronics']
```

## Source & Citation

This dataset is derived from the **Amazon Reviews 2023** dataset compiled by the McAuley Lab at UC San Diego.

**Original Dataset**: [McAuley-Lab/Amazon-Reviews-2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023)

**Project Page**: https://amazon-reviews-2023.github.io/

If you use this data, please cite the original work:

```bibtex
@article{hou2024bridging,
  title={Bridging Language and Items for Retrieval and Recommendation},
  author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian},
  journal={arXiv preprint arXiv:2403.03952},
  year={2024}
}
```

## License

This dataset follows the licensing terms of the original Amazon Reviews 2023 dataset. It is intended for research and educational purposes only.

## Intended Use

This dataset was created for the Georgia Tech Scheller College of Business course on AI in Business, specifically for assignments involving:
- Building RAG systems for product search
- Creating tool-using LLM agents
- E-commerce recommendation systems

## Limitations

- Products are filtered by review count (500+ reviews), which may introduce popularity bias
- Prices and availability may be outdated
- Not intended for production e-commerce applications