metadata

task_categories:
  - text-classification
  - feature-extraction
  - sentence-similarity
  - text2text-generation
language:
  - en
tags:
  - e-commerce
  - products
  - amazon
size_categories:
  - 100K<n<1M
license: mit

Dataset Card for Amazon Products 2023

Dataset Summary

This dataset contains product metadata from Amazon, filtered to include only products that became available in 2023. The dataset is intended for use in semantic search applications and includes a variety of product categories.

Number of Rows: 117,243
Number of Columns: 15

Data Source

The data is sourced from Amazon Reviews 2023. It includes product information across multiple categories, with additional embeddings.

Embeddings were created from the title + description using the text-embedding-3-small model.

Dataset Structure

Number of Products by Filename

	filename	product_count
0	meta_Amazon_Fashion	470
1	meta_Appliances	573
2	meta_Arts_Crafts_and_Sewing	2948
3	meta_Automotive	7161
4	meta_Baby_Products	526
5	meta_Beauty_and_Personal_Care	1402
6	meta_Books	2
7	meta_CDs_and_Vinyl	1319
8	meta_Cell_Phones_and_Accessories	5062
9	meta_Clothing_Shoes_and_Jewelry	41777
10	meta_Digital_Music	56
11	meta_Electronics	7681
12	meta_Gift_Cards	8
13	meta_Grocery_and_Gourmet_Food	96
14	meta_Handmade_Products	1018
15	meta_Health_and_Household	4760
16	meta_Health_and_Personal_Care	93
17	meta_Home_and_Kitchen	17326
18	meta_Industrial_and_Scientific	1216
19	meta_Magazine_Subscriptions	3
20	meta_Musical_Instruments	639
21	meta_Office_Products	3545
22	meta_Patio_Lawn_and_Garden	3075
23	meta_Pet_Supplies	2742
24	meta_Software	157
25	meta_Sports_and_Outdoors	6343
26	meta_Tools_and_Home_Improvement	4776
27	meta_Toys_and_Games	1367
28	meta_Unknown	541
29	meta_Video_Games	561

Columns

parent_asin (str): Unique identifier for the product.
date_first_available (datetime64[ns]): The date when the product first became available.
title (str): Title of the product.
description (str): Description of the product.
filename (str): Filename associated with the product metadata.
main_category (str): Main category of the product.
categories (List[str]): Subcategories of the product.
store (str): Store information for the product.
average_rating (float64): Average rating of the product.
rating_number (float64): Number of ratings for the product.
price (float64): Price of the product.
features (List[str]): Features of the product.
details (str): Additional details of the product. The string is JSON serializable.
embeddings (List[float64]): Embeddings generated for the product using text-embedding-3-small model.
image (str): URL of the product image.

Missing Values

main_category: 24,805 missing values
store: 253 missing values
rating_number: 6 missing values
price: 35,869 missing values

Sample Data

[
  {
    "parent_asin": "B000044U2O",
    "date_first_available": "2023-04-29T00:00:00",
    "title": "Anomie & Bonhomie",
    "description": "Amazon.com Fans of Scritti Politti's synth-pop-funk masterpiece Cupid & Psyche 85 may be shocked by how far afield Scritti mastermind Green Gartside has gone since then. Anomie & Bonhomie, his return to recording after a decadelong absence, ranges from guest shots by rappers and funksters such as Mos Def and Me'Shell Ndegeocello to Foo Fighters tributes. Gartside's trademark breathy vocals and spot-on melodicism do find their places here, but are often forced to make way for other influences. Neither a total success nor a total failure, Anomie does display a spark that makes one hope that Gartside doesn't wait so long to record again. --Rickey Wright",
    "filename": "meta_Digital_Music",
    "main_category": "Digital Music",
    "categories": [],
    "store": "Scritti Politti Format: Audio CD",
    "average_rating": 4.2,
    "rating_number": 56.0,
    "price": null,
    "features": [],
    "details": "{'Date First Available': 'April 29, 2023'}",
    "embeddings": [],
    "image": "https://m.media-amazon.com/images/I/41T618NE88L.jpg"
  },
  ...
]

Usage

This dataset can be used for various applications, including:

Semantic Search: Utilizing the embeddings to find similar products based on textual descriptions.
Product Recommendation: Enhancing recommendation systems with detailed product metadata.

Citation

@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}
}

Contact

For questions or issues regarding the dataset, please contact Amazon Reviews 2023.