license: mit
Amazon Reviews 2023 (7 Categories, Post-processed)
Overview
This dataset is a curated and post-processed subset of Amazon Reviews 2023. We select 7 product categories and apply a standard preprocessing pipeline widely used in sequential recommendation research. We adopt the official absolute-timestamp split provided by the corpus.
Included Categories
- CDs_and_Vinyl
- Video_Games
- Toys_and_Games
- Musical_Instruments
- Grocery_and_Gourmet_Food
- Arts_Crafts_and_Sewing
- Office_Products
Post-processing Pipeline
The dataset is processed per category as follows:
Positive Sample Selection: We treat user-item interactions with user ratings greater than 3 as positive samples.
K-core Filtering: To improve data quality, we remove users with fewer than 10 interactions in CDs and fewer than 5 interactions in the remaining datasets.
Directory Layout (per category)
Each category has its own folder containing:
item.csv: Primarily containing the remapped IDs for the items within that specific category.
train.csv: A file containing the interaction sequences used for training the model.
valid.csv: A dedicated directory containing the validation sequences to tune hyperparameters and prevent overfitting.
test.csv: A directory containing the test sequences.
Licensing & Attribution
This dataset is derived from Amazon Reviews 2023. Please refer to the original dataset page for licensing/usage terms and attribution requirements:
If you use this processed dataset, please cite the original dataset and clearly state that you used a post-processed subset with the pipeline described above.
Citation
If you use this dataset, please cite:
@article{mancar2026,
title={ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation},
author={Kun Yang, Yuxuan Zhu, Yazhe Chen, Siyao Zheng, Bangyang Hong, Kangle Wu, Yabo Ni, Anxiang Zeng, Cong Fu, Hui Li},
journal={arXiv preprint arXiv:2602.20093},
year={2026}
}