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.

翻译：在不断发展的电子商务领域中，推荐系统对塑造用户体验和参与度起着至关重要的作用。消费者对消费者（C2C）推荐系统的兴起，以其灵活性和对客户卖家的易访问性而著称，标志着一个重要趋势。然而，学术研究的焦点仍然主要集中在企业对消费者（B2C）模式上，而现有的有限C2C推荐数据集在商品属性、用户多样性和规模方面存在不足，留下了研究空白。C2C推荐系统的复杂性因用户同时扮演卖家和买家的双重角色而进一步凸显，这引入了一系列不那么统一且多样化的输入。针对这一问题，我们推出了MerRec，这是首个专门用于C2C推荐的大规模数据集，数据来源于Mercari电子商务平台，涵盖了2023年6个月内数百万用户和商品。MerRec不仅包含user_id、item_id和session_id等标准特征，还包含时间戳动作类型、产品分类和文本产品属性等独特元素，为研究提供了一个全面的数据集。该数据集在四项推荐任务中进行了广泛评估，为在现实场景中开发先进的推荐算法设立了新的基准，弥合了学术界与工业界之间的差距，并推动了C2C推荐的研究。我们的实验代码可在 https://github.com/mercari/mercari-ml-merrec-pub-us 获取，数据集可在 https://huggingface.co/datasets/mercari-us/merrec 获取。