license: cc-by-nc-4.0
pretty_name: MerRec
size_categories:
- 1B<n<10B
language:
- en
tags:
- recommendation
- sequential recommendation
- click-through rate prediction
- e-commerce
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