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---
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}
}
``` |