{
 "id": "123ansh/merrec",
 "owner": "123ansh",
 "name": "merrec",
 "downloads": 1475,
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 "last_modified": "2026-03-16T15:03:24.000Z",
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 "citation": "@inproceedings{10.1145/3690624.3709394,\nauthor = {Li, Lichi and Din, Zainul Abi and Tan, Zhen and London, Sam and Chen, Tianlong and Daptardar, Ajay},\ntitle = {MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems},\nyear = {2025},\nisbn = {9798400712456},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3690624.3709394},\ndoi = {10.1145/3690624.3709394},\nabstract = {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,",
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}