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README.md

@@ -1,49 +1,94 @@
 ---
-dataset_info:
-  features:
-  - name: parent_asin
-    dtype: string
-  - name: title
-    dtype: string
-  - name: description
-    sequence: string
-  - name: price
-    dtype: string
-  - name: price_numeric
-    dtype: float64
-  - name: average_rating
-    dtype: float64
-  - name: rating_number
-    dtype: int64
-  - name: features
-    sequence: string
-  - name: categories
-    sequence: string
-  - name: store
-    dtype: string
-  - name: images
-    struct:
-    - name: hi_res
-      sequence: string
-    - name: large
-      sequence: string
-    - name: thumb
-      sequence: string
-    - name: variant
-      sequence: string
-  - name: main_category
-    dtype: string
-  - name: category
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 8401058
-    num_examples: 2000
-  download_size: 4333339
-  dataset_size: 8401058
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: apache-2.0
+task_categories:
+  - text-generation
+  - feature-extraction
+language:
+  - en
+tags:
+  - e-commerce
+  - products
+  - amazon
+  - recommendations
+size_categories:
+  - 1K<n<10K
 ---
+
+# 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