journal article Open Access Jul 01, 2026

DHT-MVC4Rec: A cross-domain sequence recommendation approach based on dynamic heterogeneous graphs and multi-view contrastive learning

Array Vol. 30 pp. 100786 · Elsevier BV
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References
38
[1]
Ma M, Ren P, Lin Y, Chen Z, Ma J, Rijke Md. π-net: A parallel information-sharing network for shared-account cross-domain sequential recommendations. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. 2019, p. 685–94. 10.1145/3331184.3331200
[2]
Guo (2021)
[3]
Guo "Reinforcement learning-enhanced shared-account cross-domain sequential recommendation" IEEE Trans Knowl Data Eng (2022)
[4]
Zhang "FedDCSR: Federated cross-domain sequential recommendation via disentangled representation learning" (2024)
[5]
Li C, Zhao M, Zhang H, Yu C, Cheng L, Shu G, Kong B, Niu D. RecGURU: Adversarial learning of generalized user representations for cross-domain recommendation. In: Proceedings of the fifteenth ACM international conference on web search and data mining. 2022, p. 571–81. 10.1145/3488560.3498388
[6]
Hu G, Zhang Y, Yang Q. Conet: Collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management. 2018, p. 667–76. 10.1145/3269206.3271684
[7]
Zhu F, Chen C, Wang Y, Liu G, Zheng X. Dtcdr: A framework for dual-target cross-domain recommendation. In: Proceedings of the 28th ACM international conference on information and knowledge management. 2019, p. 1533–42. 10.1145/3357384.3357992
[8]
Li P, Tuzhilin A. Ddtcdr: Deep dual transfer cross domain recommendation. In: Proceedings of the 13th international conference on web search and data mining. 2020, p. 331–9. 10.1145/3336191.3371793
[9]
Man "Cross-domain recommendation: An embedding and mapping approach" (2017)
[10]
Zhang "Towards lightweight cross-domain sequential recommendation via external attention-enhanced graph convolution network" (2023)
[11]
Hu "MTNet: a neural approach for cross-domain recommendation with unstructured text" KDD Deep Learn Day (2018)
[12]
Cross-Domain Recommendation via Progressive Structural Alignment

Chuang Zhao, Hongke Zhao, Xiaomeng Li et al.

IEEE Transactions on Knowledge and Data Engineerin... 2023 10.1109/tkde.2023.3324912
[13]
LLMCDSR: Enhancing Cross-Domain Sequential Recommendation with Large Language Models

Haoran Xin, Ying Sun, Chao Wang et al.

ACM Transactions on Information Systems 2025 10.1145/3715099
[14]
Schlichtkrull "Modeling relational data with graph convolutional networks" (2018)
[15]
Li J, Dani H, Hu X, Tang J, Chang Y, Liu H. Attributed network embedding for learning in a dynamic environment. In: Proceedings of the 2017 ACM on conference on information and knowledge management. 2017, p. 387–96. 10.1145/3132847.3132919
[16]
Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS. Heterogeneous graph attention network. In: The world wide web conference. 2019, p. 2022–32. 10.1145/3308558.3313562
[17]
Yang "Dynamic heterogeneous graph embedding using hierarchical attentions" (2020)
[18]
Hu Z, Dong Y, Wang K, Sun Y. Heterogeneous graph transformer. In: Proceedings of the web conference 2020. 2020, p. 2704–10. 10.1145/3366423.3380027
[19]
Nguyen GH, Lee JB, Rossi RA, Ahmed NK, Koh E, Kim S. Continuous-time dynamic network embeddings. In: Companion proceedings of the the web conference 2018. 2018, p. 969–76. 10.1145/3184558.3191526
[20]
Fan Y, Ju M, Zhang C, Ye Y. Heterogeneous temporal graph neural network. In: Proceedings of the 2022 SIAM international conference on data mining (SDM), pp. 657–665. 10.1137/1.9781611977172.74
[21]
Zuo Y, Liu G, Lin H, Guo J, Hu X, Wu J. Embedding temporal network via neighborhood formation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018, p. 2857–66. 10.1145/3219819.3220054
[22]
Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X. Self-supervised graph learning for recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021, p. 726–35. 10.1145/3404835.3462862
[23]
Yu "XSimGCL: Towards extremely simple graph contrastive learning for recommendation" IEEE Trans Knowl Data Eng (2023)
[24]
Jiang Y, Huang C, Huang L. Adaptive graph contrastive learning for recommendation. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. 2023, p. 4252–61. 10.1145/3580305.3599768
[25]
Cai (2023)
[26]
Xia L, Huang C, Shi J, Xu Y. Graph-less collaborative filtering. In: Proceedings of the ACM web conference 2023. 2023, p. 17–27. 10.1145/3543507.3583196
[27]
Xie R, Liu Q, Wang L, Liu S, Zhang B, Lin L. Contrastive cross-domain recommendation in matching. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2022, p. 4226–36. 10.1145/3534678.3539125
[28]
Hou (2024)
[29]
Session-Based Recommendation with Graph Neural Networks

Shu Wu, Yuyuan Tang, Yanqiao Zhu et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v33i01.3301346
[30]
Ma C, Kang P, Liu X. Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019, p. 825–33. 10.1145/3292500.3330984
[31]
Wang J, Ding K, Hong L, Liu H, Caverlee J. Next-item recommendation with sequential hypergraphs. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020, p. 1101–10. 10.1145/3397271.3401133
[32]
Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM international conference on information and knowledge management. 2019, p. 1441–50. 10.1145/3357384.3357895
[33]
Hidasi (2015)
[34]
Xie "Contrastive learning for sequential recommendation" (2022)
[35]
Kang "Self-attentive sequential recommendation" (2018)
[36]
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation

Hourun Li, Yifan Wang, Zhiping Xiao et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v39i11.33312
[37]
Zhao C, Li X, He M, Zhao H, Fan J. Sequential recommendation via an adaptive cross-domain knowledge decomposition. In: Proceedings of the 32nd ACM international conference on information and knowledge management. 2023, p. 3453–63. 10.1145/3583780.3615058
[38]
A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation

Zhaozhao Xu, Shin-Fu Chen, Weike Pan et al.

ACM Transactions on Recommender Systems 2025 10.1145/3695884
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Published
Jul 01, 2026
Vol/Issue
30
Pages
100786
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Cite This Article
Xiang Wang, Guowei Hu, Shi Cheng (2026). DHT-MVC4Rec: A cross-domain sequence recommendation approach based on dynamic heterogeneous graphs and multi-view contrastive learning. Array, 30, 100786. https://doi.org/10.1016/j.array.2026.100786
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