---
dataset_info:
- config_name: metadata
  features:
  - name: asin
    dtype: string
  - name: title
    dtype: string
  - name: description
    dtype: string
  - name: brand
    dtype: string
  - name: categories
    sequence:
      sequence: string
  - name: price
    dtype: float64
  - name: salesRank
    struct:
    - name: Arts, Crafts & Sewing
      dtype: int64
    - name: Automotive
      dtype: int64
    - name: Baby
      dtype: int64
    - name: Beauty
      dtype: int64
    - name: Books
      dtype: int64
    - name: Camera & Photo
      dtype: int64
    - name: Cell Phones & Accessories
      dtype: int64
    - name: Clothing
      dtype: int64
    - name: Computers & Accessories
      dtype: int64
    - name: Electronics
      dtype: int64
    - name: Grocery & Gourmet Food
      dtype: int64
    - name: Health & Personal Care
      dtype: int64
    - name: Home & Kitchen
      dtype: int64
    - name: Home Improvement
      dtype: int64
    - name: Industrial & Scientific
      dtype: int64
    - name: Jewelry
      dtype: int64
    - name: Kitchen & Dining
      dtype: int64
    - name: Movies & TV
      dtype: int64
    - name: Music
      dtype: int64
    - name: Musical Instruments
      dtype: int64
    - name: Office Products
      dtype: int64
    - name: Patio, Lawn & Garden
      dtype: int64
    - name: Pet Supplies
      dtype: int64
    - name: Shoes
      dtype: int64
    - name: Software
      dtype: int64
    - name: Sports & Outdoors
      dtype: int64
    - name: Toys & Games
      dtype: int64
    - name: Video Games
      dtype: int64
    - name: Watches
      dtype: int64
  - name: imUrl
    dtype: string
  - name: also_bought
    sequence: string
  - name: also_viewed
    sequence: string
  - name: bought_together
    sequence: string
  - name: buy_after_viewing
    sequence: string
  splits:
  - name: train
    num_bytes: 746987456
    num_examples: 531694
  download_size: 268726678
  dataset_size: 746987456
- config_name: reviews
  features:
  - name: reviewerID
    dtype: string
  - name: reviewerName
    dtype: string
  - name: overall
    sequence: int64
  - name: reviewTime
    sequence: timestamp[us]
  - name: asin
    sequence: string
  - name: reviewText
    sequence: string
  - name: summary
    sequence: string
  splits:
  - name: train
    num_bytes: 1476102080
    num_examples: 1990521
  download_size: 919179623
  dataset_size: 1476102080
configs:
- config_name: metadata
  data_files:
  - split: train
    path: metadata/train-*
- config_name: reviews
  data_files:
  - split: train
    path: reviews/train-*
---
# Amazon Sports & Outdoors Dataset

## Directory Structure

- **metadata**: Contains product information.
- **reviews**: Contains user reviews about the products.
- **filtered**:
    - **e5-base-v2_embeddings.jsonl**: Contains "asin" and "embeddings" created with [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2).
    - **metadata.jsonl**: Contains "asin" and "text", where text is created from the title, description, brand, main category, and category.
    - **reviews.jsonl**: Contains "reviewerID", "reviewTime", and "asin". Reviews are filtered to include only perfect 5-star ratings with a minimum of 5 ratings.

## Usage

### Download metadata
```python
metadata = load_dataset(path="Studeni/Amazon_Sports_and_Outdoors_2014", name="metadata", split="train")
```

### Download reviews
```python
metadata = load_dataset(path="Studeni/Amazon_Sports_and_Outdoors_2014", name="reviews", split="train")
```

### Download filtered files
```
filtered_reviews = load_dataset(
    path="Studeni/Amazon_Sports_and_Outdoors_2014",
    data_files="filtered/reviews.jsonl",
    split="train",
)
```
**📎 Note:** You can set any file or list of files from the "filtered" directory as the "data_files" argument.

## Citation

### Amazon Reviews 2023
```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}
}
```
### Amazon Reviews 2018
```bibtex
@inproceedings{ni2019justifying,
  title={Justifying recommendations using distantly-labeled reviews and fine-grained aspects},
  author={Ni, Jianmo and Li, Jiacheng and McAuley, Julian},
  booktitle={Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP)},
  pages={188--197},
  year={2019}
}
```

### Amazon Reviews 2014
```bibtex
@inproceedings{he2016ups,
  title={Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering},
  author={He, Ruining and McAuley, Julian},
  booktitle={proceedings of the 25th international conference on world wide web},
  pages={507--517},
  year={2016}
}
```

```bibtex
@inproceedings{mcauley2015image,
  title={Image-based recommendations on styles and substitutes},
  author={McAuley, Julian and Targett, Christopher and Shi, Qinfeng and Van Den Hengel, Anton},
  booktitle={Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval},
  pages={43--52},
  year={2015}
}
```