IRAIFeb 22, 2024

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

arXiv:2402.14230v26 citationsh-index: 10Has CodeKDD
Originality Synthesis-oriented
AI Analysis

This dataset addresses a gap in academic research for C2C e-commerce, providing a resource for developing more effective recommendation systems in this domain.

The authors tackled the lack of large-scale datasets for Consumer-to-Consumer (C2C) recommendation systems by introducing MerRec, a dataset sourced from Mercari covering millions of users and products over 6 months, which establishes a new benchmark for advanced algorithms in real-world scenarios.

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.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes