Page 436 - 《软件学报》2025年第9期
P. 436
钱忠胜 等: 利用伪重叠判定机制的多层循环 GCN 跨域推荐 4347
10747]
[8] Zhou KX, Huang X, Li YN, Zha DC, Chen R, Hu X. Towards deeper graph neural networks with differentiable group normalization. In:
Proc. of the 34th Int’l Conf. on Neural Information Processing Systems. Vancouver: ACM, 2020. 413.
[9] Huang L, Huang ZW, Huang ZY, Guan CR, Gao YF, Wang CD. Graph convolutional broad cross-domain recommender system. Journal
of Computer Research and Development, 2024, 61(7): 1713–1729. (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.
202330617]
[10] Gao C, Zheng Y, Li N, Li YF, Qin YR, Piao JH, Quan YH, Chang JX, Jin DP, He XN, Li Y. A survey of graph neural networks for
recommender systems: challenges, methods, and directions. ACM Trans. on Recommender Systems, 2023, 1(1): 3. [doi: 10.1145/
3568022]
[11] Jin H, Hu ZQ. The non-negative matrix factorization based algorithm for community detection in sparse networks. Acta Electronica
Sinica, 2023, 51(10): 2950–2959 (in Chinese with English abstract). [doi: 10.12263/DZXB.20210950]
[12] Afoudi Y, Lazaar M, Al Achhab M. Hybrid recommendation system combined content-based filtering and collaborative prediction using
artificial neural network. Simulation Modelling Practice and Theory, 2021, 113: 102375. [doi: 10.1016/j.simpat.2021.102375]
[13] Siet S, Peng S, Ilkhomjon S, Kang MS, Park DS. Enhancing sequence movie recommendation system using deep learning and kmeans.
Applied Sciences, 2024, 14(6): 2505. [doi: 10.3390/app14062505]
[14] Chang JX, Gao C, Zheng Y, Hui YQ, Niu YN, Song Y, Jin DP, Li Y. Sequential recommendation with graph neural networks. In: Proc.
of the 44th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. New York: ACM, 2021. 378–387. [doi: 10.
1145/3404835.3462968]
[15] Kang SK, Hwang J, Lee D, Yu H. Semi-supervised learning for cross-domain recommendation to cold-start users. In: Proc. of the 28th
ACM Int’l Conf. on Information and Knowledge Management. Beijing: ACM, 2019. 1563–1572. [doi: 10.1145/3357384.3357914]
[16] Xu J, Wang X, Zhang HM, Lv P. Heterogeneous and clustering-enhanced personalized preference transfer for cross-domain
recommendation. Information Fusion, 2023, 99: 101892. [doi: 10.1016/j.inffus.2023.101892]
[17] Liu WM, Chen CC, Liao XT, Hu ML, Yin JW, Tan YC, Zheng LF. Federated probabilistic preference distribution modelling with
compactness co-clustering for privacy-preserving multi-domain recommendation. In: Proc. of the 32nd Int’l Joint Conf. on Artificial
Intelligence. Macao: IJCAI, 2023. 2206–2214. [doi: 10.24963/ijcai.2023/245]
[18] Wang X, He XN, Nie LQ, Chua TS. Item silk road: recommending items from information domains to social users. In: Proc. of the 40th
Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Shinjuku: ACM, 2017. 185–194. [doi: 10.1145/3077136.
3080771]
[19] Cui Q, Wei T, Zhang YF, Zhang Q. HeroGRAPH: a heterogeneous graph framework for multi-target cross-domain recommendation. In:
Proc. of the 3rd Workshop on Online Recommender Systems and User Modeling Co-located with the 14th ACM Conf. on Recommender
Systems. New York: CEUR, 2020.
[20] Xu K, Xie YZ, Chen L, Zheng ZB. Expanding relationship for cross domain recommendation. In: Proc. of the 30th ACM Int’l Conf. on
Information & Knowledge Management. New York: ACM, 2021. 2251–2260. [doi: 10.1145/3459637.3482429]
[21] Li J, Peng ZH, Wang SZ, Xu XK, Yu PS, Hao ZY. Heterogeneous graph embedding for cross-domain recommendation through
adversarial learning. In: Proc. of the 25th Int’l Conf. on Database Systems for Advanced Applications. Jeju: Springer, 2020. 507–522.
[doi: 10.1007/978-3-030-59419-0_31]
[22] Cao JX, Sheng JW, Cong X, Liu TW, Wang B. Cross-domain recommendation to cold-start users via variational information bottleneck.
In: Proc. of the 38th Int’l Conf. on Data Engineering. Kuala Lumpur: IEEE, 2022. 2209–2223. [doi: 10.1109/ICDE53745.2022.00211]
[23] Mutiara, Mutiara AB, Wirawan S, Yusnitasari T, Anggraini D. Expanding louvain algorithm for clustering relationship formation. Int’l
Journal of Advanced Computer Science and Applications, 2023, 14(1): 701–708. [doi: 10.14569/IJACSA.2023.0140177]
[24] Liu Q, Cheng Y. Research on multi-granularity neural network pruning method with regularization mechanism. Acta Electronica Sinica,
2023, 51(8): 2202–2212 (in Chinese with English abstract). [doi: 10.12263/DZXB.20210844]
[25] Zhou JK, Wang N, Cui L. EasiLTOM: signal activity interval recognition based on local dynamic threshold. Journal of Computer
Research and Development, 2022, 59(4): 826–833 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.20200662]
[26] Hsieh CK, Yang LQ, Cui Y, Lin TY, Belongie S, Estrin D. Collaborative metric learning. In: Proc. of the 26th Int’l Conf. on World Wide
Web. Perth: ACM, 2017. 193–201. [doi: 10.1145/3038912.3052639]
[27] Cui ZH, Xu XH, Xue F, Cai XJ, Cao Y, Zhang WS, Chen JJ. Personalized recommendation system based on collaborative filtering for
IoT scenarios. IEEE Trans. on Services Computing, 2020, 13(4): 685–695. [doi: 10.1109/TSC.2020.2964552]
[28] Wang X, He XN, Wang M, Feng FL, Chua TS. Neural graph collaborative filtering. In: Proc. of the 42nd Int’l ACM SIGIR Conf. on
Research and Development in Information Retrieval. Paris: ACM, 2019. 165–174. [doi: 10.1145/3331184.3331267]

