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---
license: gpl-3.0
language:
- en
tags:
- recommendation
- reviews
- ecommerce
- ratings
- user-behavior
pretty_name: Amazon Reviews 2023 All-Category k-Core
size_categories:
- 100M<n<1B
---
# Dataset Card for `Amazon Reviews 2023 All-Category k-Core`
* These datasets are subsets of [Amazon reviews dataset](https://amazon-reviews-2023.github.io/), collected in 2023 by [McAuley Lab](https://cseweb.ucsd.edu/~jmcauley/).
* It contains **all categories** of the reviews from the original dataset that have more than $k \in [5, 20]$ interactions.
* The original dataset contains reviews in the period of May. 1996 to Sep. 2023.
* The reviews are grouped into 25 categories.
* The dataset is in `.parquet` format.
> [!NOTE]
>
> **k-core** means that every user and every item has at least k interactions across **ALL categories combined**.
>
> This condition may not hold within a single category.
>
## Dataset Details
### Dataset Description
The dataset contains reviews from Amazon, and it is a subset of the original dataset. The dataset is in `.parquet` format.
Please refer to the [Dataset Creation and Processing](#dataset-creation-and-processing) section for more details about the dataset.
### Dataset Structure
The repository is structured as follows:
```
amazon-2023-all-category-k-core/
|- 5-core/
|- 5-core.parquet # 5-core ratings of all categories, 3.16GB
|- 20-core/
|- category/
|- Arts_Crafts_and_Sewing/
|- ratings.parquet # ratings of Arts, Crafts & Sewing
|- meta.parquet # meta data of items in Arts, Crafts & Sewing
|- reviews.parquet # reviews of items in Arts, Crafts & Sewing
|- ... # other categories
|- 20-core.parquet # 20-core ratings of all categories, 1.1GB
|- item_map.jsonl.gz # item map, format: [{item_index:int, parent_asin:str}], 7.97MB
|- user_map.jsonl.gz # user map, format: [{user_index:int, user_id:str}], 29.4MB
```
## Dataset Creation and Processing
1. Merge the `ratings` from all categories of [Amazon reviews 2023 dataset](https://amazon-reviews-2023.github.io/)
2. Filter out the `ratings` that have less than $k$ interactions, where $k \in [5, 20]$.
3. Filter out the `meta` data and `reviews` of items that are not in the filtered `ratings`.
4. Save the datasets in `.parquet` format.
## Dataset Sources
The original dataset is available at [Amazon reviews dataset](https://amazon-reviews-2023.github.io/).
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## Uses
This dataset can be used for recommendation systems, sentiment analysis, and other NLP tasks.
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## Glossary
The glossary of the dataset is available at [Amazon Reviews#Data Fields](https://amazon-reviews-2023.github.io/#data-fields).
## Dataset Card Authors
Chenglong Ma
## Dataset Card Contact
https://huggingface.co/ChenglongMa