metadata

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.
- 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

metadata = load_dataset(path="smartcat/Amazon_All_Beauty_2018", name="metadata", split="train")

Download reviews

metadata = load_dataset(path="smartcat/Amazon_All_Beauty_2018", name="reviews", split="train")

Download filtered files

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

@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

@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

@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}
}

@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}
}