MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems

This repository contains the dataset accompanying the paper MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems.

Contributors: Lichi Li, Zainul Abi Din, Zhen Tan, Sam London, Tianlong Chen, Ajay Daptardar

Overview

The MerRec dataset is a large-scale, highly diverse, thoroughly anonymized and derived subset of item interaction event sequence data from Mercari, the C2C marketplace e-commerce platform. It is designed for researchers to study recommendation related tasks on a rich C2C environment with many item features.

Some basic statistics are:

• Unique users: Over 5 million
• Unique items: Over 80 million
• Unique events: Over 1 billion
• Unique sessions: Over 200 million
• Item title text tokens: Over 8 billion

For a detailed walkthrough and an extensive list of accurate statistics, feature interpretations, preprocessing procedure, please refer to the paper.

File Organization

The MerRec dataset is divided into 6 directories, each containing about 300 Parquet shards from a particular month in 2023.

Experiments

Code implementation used for the experiment section of the paper can be found here.

BibTeX

@misc{li2024merrec,
      title={MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems}, 
      author={Lichi Li and Zainul Abi Din and Zhen Tan and Sam London and Tianlong Chen and Ajay Daptardar},
      year={2024},
      eprint={2402.14230},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

License

Dataset license: CC BY-NC 4.0 International