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

TL;DR

MerRec is a large-scale, comprehensive dataset from Mercari designed to advance research in consumer-to-consumer recommendation systems, addressing previous data limitations and establishing new benchmarks for real-world applications.

Contribution

This paper introduces MerRec, the first extensive C2C recommendation dataset with diverse features, enabling improved research and development of recommendation algorithms.

Findings

MerRec covers millions of users and products over 6 months.

The dataset includes detailed item and user attributes, enhancing research depth.

Extensive evaluation across four recommendation tasks demonstrates its utility.

Abstract

In the evolving e-commerce field, recommendation systems crucially shape user experience and engagement. The rise of Consumer-to-Consumer (C2C) recommendation systems, noted for their flexibility and ease of access for customer vendors, marks a significant trend. However, the academic focus remains largely on Business-to-Consumer (B2C) models, leaving a gap filled by the limited C2C recommendation datasets that lack in item attributes, user diversity, and scale. The intricacy of C2C recommendation systems is further accentuated by the dual roles users assume as both sellers and buyers, introducing a spectrum of less uniform and varied inputs. Addressing this, we introduce MerRec, the first large-scale dataset specifically for C2C recommendations, sourced from the Mercari e-commerce platform, covering millions of users and products over 6 months in 2023. MerRec not only includes standard features such as user_id, item_id, and session_id, but also unique elements like timestamped action types, product taxonomy, and textual product attributes, offering a comprehensive dataset for research. This dataset, extensively evaluated across four recommendation tasks, establishes a new benchmark for the development of advanced recommendation algorithms in real-world scenarios, bridging the gap between academia and industry and propelling the study of C2C recommendations. Our experiment code is available at https://github.com/mercari/mercari-ml-merrec-pub-us and dataset at https://huggingface.co/datasets/mercari-us/merrec.