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    <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.</abstract>
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%0 Conference Proceedings
%T Cross-user Collaborative and Sequential Modeling for Recommendation
%A He, Qiao-Ying
%A Chen, Yi-En
%A Chen, Kuan-Yu
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F he-etal-2025-cross
%X 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.
%U https://aclanthology.org/2025.rocling-main.24/
%P 230-236

Markdown (Informal)

[Cross-user Collaborative and Sequential Modeling for Recommendation](https://aclanthology.org/2025.rocling-main.24/) (He et al., ROCLING 2025)

• Cross-user Collaborative and Sequential Modeling for Recommendation (He et al., ROCLING 2025)

ACL

• Qiao-Ying He, Yi-En Chen, and Kuan-Yu Chen. 2025. Cross-user Collaborative and Sequential Modeling for Recommendation. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 230–236, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.