Update README.md

README.md

@@ -14,7 +14,7 @@ tags:
 
 # MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
 
-This repository contains the dataset accompanying the paper [MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems](https://arxiv.org/abs/2402.14230).
+This repository contains the dataset accompanying the paper [MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems](https://dl.acm.org/doi/10.1145/3690624.3709394) at KDD 2025.
 
 Contributors: Lichi Li, Zainul Abi Din, Zhen Tan, Sam London, Tianlong Chen, Ajay Daptardar
 
@@ -42,14 +42,25 @@ Code implementation used for the experiment section of the paper can be found [h
 
 ## BibTeX
 
+If you found our work useful, please consider citing MerRec:
+
 ```bibtex
-@misc{li2024merrec,
-      title={MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems}, 
-      author={Lichi Li and Zainul Abi Din and Zhen Tan and Sam London and Tianlong Chen and Ajay Daptardar},
-      year={2024},
-      eprint={2402.14230},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR}
+@inproceedings{10.1145/3690624.3709394,
+author = {Li, Lichi and Din, Zainul Abi and Tan, Zhen and London, Sam and Chen, Tianlong and Daptardar, Ajay},
+title = {MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems},
+year = {2025},
+isbn = {9798400712456},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3690624.3709394},
+doi = {10.1145/3690624.3709394},
+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 three 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. Experiment code (https://github.com/mercari/mercari-ml-merrec-pub-us) and dataset (https://huggingface.co/datasets/mercari-us/merrec) are released.},
+booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1},
+pages = {2371–2382},
+numpages = {12},
+keywords = {datasets, recommender systems},
+location = {Toronto ON, Canada},
+series = {KDD '25}
 }
 ```