@inproceedings{he-etal-2025-cross,
    title = "Cross-user Collaborative and Sequential Modeling for Recommendation",
    author = "He, Qiao-Ying  and
      Chen, Yi-En  and
      Chen, Kuan-Yu",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.rocling-main.24/",
    pages = "230--236",
    ISBN = "979-8-89176-379-1",
    abstract = "Multi-behavior recommendation leverages auxiliary behaviors to effectively alleviate the sparsity of target behaviors. Existing approaches can be broadly categorized into two paradigms: sequential models that capture individual temporal dynamics but often omit cross-user information, and graph-based models that mine collaborative patterns yet lack temporal dependency modeling. To address these limitations, this paper proposes an integrated approach that combines sequential and graph modeling: the former focuses on learning temporal dependencies within user behavior sequences, while the latter captures cross-user behavior paths. By fusing the predictions from both components, the method achieves more accurate recommendations. Experiments on two e-commerce datasets, Taobao and RetailRocket, show that the integrated model outperforms the strong baseline MB-STR by about 1{\%} in both HR@10 and NDCG@10. These results indicate that incorporating cross-user collaborative information consistently improves performance, even on top of strong sequential models."
}