---
dataset_info:
- config_name: metadata
  features:
  - name: asin
    dtype: string
  - name: title
    dtype: string
  - name: description
    dtype: string
  - name: brand
    dtype: string
  - name: main_cat
    dtype: string
  - name: category
    sequence: 'null'
  - name: also_buy
    sequence: string
  - name: also_view
    sequence: string
  - name: imageURL
    sequence: string
  - name: imageURLHighRes
    sequence: string
  splits:
  - name: train
    num_bytes: 21323873
    num_examples: 32891
  download_size: 9685233
  dataset_size: 21323873
- 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: 3055300
    num_examples: 1398
  download_size: 1191665
  dataset_size: 3055300
configs:
- config_name: metadata
  data_files:
  - split: train
    path: metadata/train-*
- config_name: reviews
  data_files:
  - split: train
    path: reviews/train-*
---

# Amazon All Beauty 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="smartcat/Amazon_All_Beauty_2018", name="metadata", split="train")
```

### Download reviews
```python
metadata = load_dataset(path="smartcat/Amazon_All_Beauty_2018", name="reviews", split="train")
```

### Download filtered files
```python
filtered_reviews = load_dataset(
    path="smartcat/Amazon_All_Beauty_2018",
    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}
}
```