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 six 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.

MerRec 是第一个针对 C2C 推荐的大规模数据集，包含用户、产品和时间戳等标准特征，以及商品分类和文本属性等独特元素，通过六个推荐任务的广泛评估，为研究 C2C 推荐算法在实际场景中的发展建立了一个新的基准，弥合了学术界和工业界之间的差距。