@inproceedings{zheng-etal-2025-fedcsr,
    title = "{F}ed{CSR}: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning",
    author = "Zheng, Dongyi  and
      Zhang, Hongyu  and
      Zhai, Jianyang  and
      Zhong, Lin  and
      Wang, Lingzhi  and
      Feng, Jiyuan  and
      Liao, Xiangke  and
      Tian, Yonghong  and
      Xiao, Nong  and
      Liao, Qing",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2025.coling-main.581/",
    pages = "8699--8713",
    abstract = "Cross-domain sequential recommendation (CSR) has garnered significant attention. Current federated frameworks for CSR leverage information across multiple domains but often rely on user alignment, which increases communication costs and privacy risks. In this work, we propose FedCSR, a novel federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. FedCSR fully utilizes cross-domain knowledge to address the key challenges related to data heterogeneity both inter- and intra-platform. To tackle the heterogeneity of data patterns between platforms, we introduce Model Contrastive Learning (MCL) to reduce the gap between local and global models. Additionally, we design Sequence Contrastive Learning (SCL) to address the heterogeneity of user preferences across different domains within a platform by employing tailored sequence augmentation techniques. Extensive experiments conducted on multiple real-world datasets demonstrate that FedCSR achieves superior performance compared to existing baseline methods."
}