--- 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 [!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/). ## Uses This dataset can be used for recommendation systems, sentiment analysis, and other NLP tasks. ## 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