{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:24:31Z","timestamp":1775503471378,"version":"3.50.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s10586-026-05941-0","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T18:39:30Z","timestamp":1775500770000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing federated recommendations with personalized contrastive learning on graph neural networks"],"prefix":"10.1007","volume":"29","author":[{"given":"Nuha Mohammed","family":"Alshuqayran","sequence":"first","affiliation":[]},{"given":"Abbas N.","family":"Talib","sequence":"additional","affiliation":[]},{"given":"Narinderjit Singh","family":"Sawaran Singh","sequence":"additional","affiliation":[]},{"given":"Nidhi","family":"Sharma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,6]]},"reference":[{"issue":"1","key":"5941_CR1","first-page":"1","volume":"1","author":"Yu Chen Gao","year":"2023","unstructured":"Chen Gao, Yu., Zheng, N.L., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., et al.: A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems 1(1), 1\u201351 (2023)","journal-title":"ACM Transactions on Recommender Systems"},{"issue":"1","key":"5941_CR2","first-page":"1","volume":"22","author":"X Gan","year":"2025","unstructured":"Gan, X.: Graphservice: Topology-aware constructor for large-scale graph applications. ACM Transactions on Architecture and Code Optimization 22(1), 1\u201324 (2025)","journal-title":"ACM Transactions on Architecture and Code Optimization"},{"issue":"4","key":"5941_CR3","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1109\/TPDS.2021.3100785","volume":"33","author":"X Gan","year":"2021","unstructured":"Gan, X., Zhang, Y., Wang, R., Li, T., Xiao, T., Zeng, R., Liu, J., Kai, L.: Tianhegraph: Customizing graph search for graph500 on tianhe supercomputer. IEEE Trans. Parallel Distrib. Syst. 33(4), 941\u2013951 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"5941_CR4","doi-asserted-by":"crossref","unstructured":"Qian, Chen., Zhiqiang, Guo., Jianjun, Li., and Guohui, Li.: Knowledge-enhanced multi-view graph neural networks for session-based recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 352\u2013361, (2023)","DOI":"10.1145\/3539618.3591706"},{"issue":"9","key":"5941_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4289-6","volume":"68","author":"J Liu","year":"2025","unstructured":"Liu, J., Jiang, G., Chu, C., Li, Y., Wang, Z., Shuyue, H.: A formal model for multiagent q-learning on graphs. SCIENCE CHINA Inf. Sci. 68(9), 192206 (2025)","journal-title":"SCIENCE CHINA Inf. Sci."},{"key":"5941_CR6","doi-asserted-by":"crossref","unstructured":"Angela Di\u00a0Fazio. Enhancing privacy in recommender systems through differential privacy techniques. In Proceedings of the 18th ACM Conference on Recommender Systems, pages 1348\u20131352, (2024)","DOI":"10.1145\/3640457.3688019"},{"key":"5941_CR7","unstructured":"Brendan, McMahan., Eider, Moore., Daniel, Ramage., Seth, Hampson., and Blaise\u00a0Aguera y,\u00a0Arcas.: Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273\u20131282. PMLR, (2017)"},{"key":"5941_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107381","volume":"188","author":"Y Cao","year":"2025","unstructured":"Cao, Y., Shi, F., Qing, Y., Lin, X., Zhou, C., Zou, L., Zhang, P., Li, Z., Yin, D.: Ibpl: information bottleneck-based prompt learning for graph out-of-distribution detection. Neural Netw. 188, 107381 (2025)","journal-title":"Neural Netw."},{"issue":"6","key":"5941_CR9","doi-asserted-by":"publisher","first-page":"2938","DOI":"10.1109\/TCSS.2022.3230987","volume":"10","author":"X Zeng","year":"2022","unstructured":"Zeng, X., Zhou, T., Bao, Z., Zhao, H., Chen, L., Wang, X., Wang, F.: Feature-contrastive graph federated learning: Responsible ai in graph information analysis. IEEE Transactions on Computational Social Systems 10(6), 2938\u20132948 (2022)","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"5941_CR10","unstructured":"Muhammad, Ammad-Ud-Din., Elena, Ivannikova., Suleiman\u00a0A, Khan., Were., Oyomno, Qiang Fu., Kuan\u00a0Eeik, Tan., and Adrian, Flanagan.: Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888, (2019)"},{"key":"5941_CR11","unstructured":"Yujiao, Hu., Yuan, Yao., Jinchao, Chen., Zhihao, Wang., Qingmin, Jia., and Yan, Pan.: Solving scalable multiagent routing problems with reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, (2025)"},{"issue":"5","key":"5941_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/MIS.2020.3014880","volume":"36","author":"D Chai","year":"2020","unstructured":"Chai, D., Wang, L., Chen, K., Yang, Q.: Secure federated matrix factorization. IEEE Intell. Syst. 36(5), 11\u201320 (2020)","journal-title":"IEEE Intell. Syst."},{"key":"5941_CR13","doi-asserted-by":"crossref","unstructured":"Xiaoyao, Zheng., Manping, Guan., Xianmin, Jia., Liping, Sun., and Yonglong, Luo.: Federated matrix factorization recommendation based on secret sharing for privacy preserving. IEEE Transactions on Computational Social Systems, (2023)","DOI":"10.1109\/TCSS.2023.3322824"},{"key":"5941_CR14","doi-asserted-by":"crossref","unstructured":"Sichun, Luo., Yuanzhang, Xiao., and Linqi, Song.: Personalized federated recommendation via joint representation learning, user clustering, and model adaptation. In Proceedings of the 31st ACM international conference on information & knowledge management, pages 4289\u20134293, (2022)","DOI":"10.1145\/3511808.3557668"},{"key":"5941_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111447","volume":"163","author":"W Song","year":"2025","unstructured":"Song, W., Ye, Z., Sun, M., Hou, X., Li, S., Hao, A.: Attridiffuser: Adversarially enhanced diffusion model for text-to-facial attribute image synthesis. Pattern Recognition 163, 111447 (2025)","journal-title":"Pattern Recognition"},{"key":"5941_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108441","volume":"242","author":"P Vasileios","year":"2022","unstructured":"Vasileios, P., Efraimidis., P.S.: Federated neural collaborative filtering. Knowledge-Based Systems 242, 108441 (2022)","journal-title":"Knowledge-Based Systems"},{"key":"5941_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2023.103996","volume":"324","author":"H Pengqing","year":"2023","unstructured":"Pengqing, H., Lin, Z., Pan, W., Yang, Q., Peng, X., Ming, Z.: Privacy-preserving graph convolution network for federated item recommendation. Artif. Intell. 324, 103996 (2023)","journal-title":"Artif. Intell."},{"issue":"6","key":"5941_CR18","doi-asserted-by":"publisher","first-page":"3835","DOI":"10.1109\/TNSE.2021.3093782","volume":"9","author":"R Zhou","year":"2021","unstructured":"Zhou, R., Qian, H., Zhang, J., Liu, C., Wan, J., Ren, Y., Xiong, N., Zhao, N., Zhang, S.: Self-attention mechanism enhanced user interests modeling for personalized recommendation services in cyber-physical-social systems. IEEE Transactions on Network Science and Engineering 9(6), 3835\u20133846 (2021)","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"5941_CR19","doi-asserted-by":"crossref","unstructured":"Christopher, Briggs., Zhong, Fan., and Peter, Andras.: Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In 2020 international joint conference on neural networks (IJCNN), pages 1\u20139. IEEE, (2020)","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"5941_CR20","doi-asserted-by":"crossref","unstructured":"Chunxu, Zhang., Guodong, Long., Tianyi, Zhou., Peng, Yan., Zijian, Zhang., Chengqi, Zhang., and Bo,\u00a0Yang.: Dual personalization on federated recommendation. arXiv preprint arXiv:2301.08143, (2023)","DOI":"10.24963\/ijcai.2023\/507"},{"issue":"3","key":"5941_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560486","volume":"41","author":"M Imran","year":"2023","unstructured":"Imran, M., Yin, H., Chen, T., Nguyen, Q.V.H., Zhou, A., Zheng, K.: Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences. ACM Transactions on Information Systems 41(3), 1\u201330 (2023)","journal-title":"ACM Transactions on Information Systems"},{"key":"5941_CR22","unstructured":"Zhexiao, Cao., Lei, Huang., Tian, Wang., Yinquan, Wang., Jingang, Shi., Aichun, Zhu., Tianyun, Shi., and Hichem, Snoussi.: Understanding the dimensional need of noncontrastive learning. IEEE Transactions on Cybernetics, (2025)"},{"key":"5941_CR23","doi-asserted-by":"crossref","unstructured":"Liangbo, Ning., Zuowei, Zhang., Weiping, Ding., Dian, Shao., and Yining, Zhu.: Multilevel distribution alignment for multisource universal domain adaptation. IEEE Transactions on Neural Networks and Learning Systems, (2025)","DOI":"10.1109\/TNNLS.2025.3561401"},{"issue":"2","key":"5941_CR24","first-page":"913","volume":"36","author":"Y Junliang","year":"2023","unstructured":"Junliang, Y., Xia, X., Chen, T., Cui, L., Hung, N.Q.V., Yin, H.: Xsimgcl: Towards extremely simple graph contrastive learning for recommendation. IEEE Trans. Knowl. Data Eng. 36(2), 913\u2013926 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5941_CR25","unstructured":"Chuhan, Wu., Fangzhao, Wu., Tao, Qi., Yongfeng, Huang., and Xing, Xie.: Fedcl: Federated contrastive learning for privacy-preserving recommendation. arXiv preprint arXiv:2204.09850, (2022)"},{"key":"5941_CR26","doi-asserted-by":"crossref","unstructured":"Linze, Luo., Baisong, Liu.: Dual-contrastive for federated social recommendation. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1\u20138. IEEE, (2022)","DOI":"10.1109\/IJCNN55064.2022.9892278"},{"key":"5941_CR27","doi-asserted-by":"crossref","unstructured":"Tao, Hai., Ali, Basem., As\u2019\u00a0ad, Alizadeh., Kamal, Sharma., Dheyaa\u00a0J, Jasim., Husam Rajab., Abdelkader, Mabrouk., Lioua, Kolsi., Wajdi, Rajhi., Hamid, Maleki, et\u00a0al.: Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving mxene-based ionanofluids applicable in pv\/t solar systems. Scientific Reports, 14(1):29524, (2024)","DOI":"10.1038\/s41598-024-81044-3"},{"key":"5941_CR28","doi-asserted-by":"crossref","unstructured":"Changqin, Huang., Chengling, Gao., Ming, Li., Yongzhi, Li., Xizhe, Wang., Yunliang, Jiang., and Xiaodi, Huang.: Correlation information enhanced graph anomaly detection via hypergraph transformation. IEEE transactions on cybernetics, (2025)","DOI":"10.1109\/TCYB.2025.3558941"},{"issue":"1","key":"5941_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-64234-x","volume":"14","author":"D Fude","year":"2024","unstructured":"Fude, D., Mahdiyeh, E., Mohammad, K., Ali Basem, D.J.J., Sivaprakasam, P.: Optimization of a photovoltaic\/wind\/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved kepler optimizer algorithms. Scientific Reports 14(1), 13354 (2024)","journal-title":"Scientific Reports"},{"key":"5941_CR30","doi-asserted-by":"crossref","unstructured":"Narinderjit Singh\u00a0Sawaran Singh, Abbas\u00a0N Talib, Karwan\u00a0Hussein Qader, Nidhi Sharma, and Zhang Feng. Fault-tolerance-aware flexible and mobility-aware task offloading based on machine learning in mobile cloud computing. International Journal of Data Science and Analytics, pages 1\u201320, (2025)","DOI":"10.1007\/s41060-025-00815-x"},{"key":"5941_CR31","doi-asserted-by":"crossref","unstructured":"Xiangjun, Wu., Shuo, Ding., Ning, Zhao., Huanqing, Wang., and Ben, Niu.: Neural-network-based event-triggered adaptive secure fault-tolerant containment control for nonlinear multi-agent systems under denial-of-service attacks. Neural Networks, 107725, (2025)","DOI":"10.1016\/j.neunet.2025.107725"},{"key":"5941_CR32","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1093\/ijlct\/ctae098","volume":"19","author":"X Saihua","year":"2024","unstructured":"Saihua, X., Basem, A., Al-Asadi, H.A., Chaturvedi, R., Daminova, G., Fouad, Y., Jasim, D.J., Alhoee, J.: Employing deep learning for predicting the thermal properties of water and nano-encapsulated phase change material. International Journal of Low-Carbon Technologies 19, 1453\u20131459 (2024)","journal-title":"International Journal of Low-Carbon Technologies"},{"issue":"1","key":"5941_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad431","volume":"25","author":"Y Meng","year":"2024","unstructured":"Meng, Y., Wang, Y., Xu, J., Lu, C., Tang, X., Peng, T., Zhang, B., Tian, G., Yang, J.: Drug repositioning based on weighted local information augmented graph neural network. Brief. Bioinform. 25(1), bbad431 (2024)","journal-title":"Brief. Bioinform."},{"key":"5941_CR34","doi-asserted-by":"crossref","unstructured":"Joy, Djuansjah., Ihab, Omar., As\u2019\u00a0ad ,Alizadeh., Abdellatif\u00a0M, Sadeq., Shaymaa\u00a0Abed, Hussein., Narinderjit, Singh\u00a0Sawaran Singh., Husam, Rajab., and Khalil, Hajlaoui.: Dynamic mode decomposition-based surrogate modeling of wall shear stress in an aneurysm artery. Physics of Fluids, 37(8), (2025)","DOI":"10.1063\/5.0284372"},{"issue":"1","key":"5941_CR35","volume":"2022","author":"F Zeng","year":"2022","unstructured":"Zeng, F., Tang, R., Wang, Y.: User personalized recommendation algorithm based on gru network model in social networks. Mob. Inf. Syst. 2022(1), 1487586 (2022)","journal-title":"Mob. Inf. Syst."},{"key":"5941_CR36","doi-asserted-by":"crossref","unstructured":"Steffen, Rendle., Walid, Krichene., Li,\u00a0Zhang., and John, Anderson.: Neural collaborative filtering vs. matrix factorization revisited. In Proceedings of the 14th ACM Conference on Recommender Systems, pages 240\u2013248, (2020)","DOI":"10.1145\/3383313.3412488"},{"key":"5941_CR37","unstructured":"Miao, Yu., Tianqi, Quan., Qinglong, Peng., Xu,\u00a0Yu., and Lei, Liu.: A model-based collaborate filtering algorithm based on stacked autoencoder. Neural Computing and Applications, pages 1\u20139, (2022)"},{"issue":"1","key":"5941_CR38","doi-asserted-by":"publisher","first-page":"130","DOI":"10.5829\/IJE.2023.36.01A.15","volume":"36","author":"G Spoorthy","year":"2023","unstructured":"Spoorthy, G., Sanjeevi, S.G.: Multi-criteria-recommendations using autoencoder and deep neural networks with weight optimization using firefly algorithm. Int. J. Eng. 36(1), 130\u2013138 (2023)","journal-title":"Int. J. Eng."},{"key":"5941_CR39","doi-asserted-by":"crossref","unstructured":"Wenqi, Fan., Yao, Ma., Qing, Li., Yuan, He., Eric, Zhao., Jiliang, Tang., and Dawei, Yin.: Graph neural networks for social recommendation. In The world wide web conference, pages 417\u2013426, (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"5941_CR40","doi-asserted-by":"crossref","unstructured":"Tian Xie, Chaoyang He, Xiang Ren, Cyrus Shahabi, and C-C\u00a0Jay Kuo. L-bgnn: Layerwise trained bipartite graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, (2022)","DOI":"10.1109\/TNNLS.2022.3171199"},{"key":"5941_CR41","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s41019-016-0020-2","volume":"1","author":"S Badsha","year":"2016","unstructured":"Badsha, S., Yi, X., Khalil, I.: A practical privacy-preserving recommender system. Data Science and Engineering 1, 161\u2013177 (2016)","journal-title":"Data Science and Engineering"},{"issue":"3","key":"5941_CR42","first-page":"3169","volume":"45","author":"X Xinyi","year":"2022","unstructured":"Xinyi, X., Deng, C., Xie, Y., Ji, S.: Group contrastive self-supervised learning on graphs. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3169\u20133180 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5941_CR43","doi-asserted-by":"crossref","unstructured":"Jian Wang, Chenglong Wang, Lin Guo, Shuchang Zhao, Dandan Wang, Shiqing Zhang, Xiaoming Zhao, Jun Yu, Yaowei Wang, Yi\u00a0Yang, et\u00a0al. Mdkat: Multimodal decoupling with knowledge aggregation and transfer for video emotion recognition. IEEE Transactions on Circuits and Systems for Video Technology, (2025)","DOI":"10.1109\/TCSVT.2025.3571534"},{"key":"5941_CR44","unstructured":"Aaron van\u00a0den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, (2018)"},{"key":"5941_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108548","volume":"133","author":"Z Yin","year":"2024","unstructured":"Yin, Z., Wang, S.: Enhancing bibliographic reference parsing with contrastive learning and prompt learning. Eng. Appl. Artif. Intell. 133, 108548 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5941_CR46","doi-asserted-by":"crossref","unstructured":"Ding, Zou., Wei, Wei., Xian-Ling, Mao., Ziyang, Wang., Minghui, Qiu., Feida, Zhu., and Xin, Cao.: Multi-level cross-view contrastive learning for knowledge-aware recommender system. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, 1358\u20131368, (2022)","DOI":"10.1145\/3477495.3532025"},{"key":"5941_CR47","unstructured":"Xuheng, Cai., Chao, Huang., Lianghao, Xia., and Xubin, Ren.: Lightgcl: Simple yet effective graph contrastive learning for recommendation. arXiv preprint arXiv:2302.08191, (2023)"},{"key":"5941_CR48","doi-asserted-by":"crossref","unstructured":"Yiqing, Wu., Ruobing, Xie., Yongchun, Zhu., Xiang, Ao., Xin, Chen., Xu,\u00a0Zhang., Fuzhen, Zhuang., Leyu, Lin., and Qing, He.: Multi-view multi-behavior contrastive learning in recommendation. In International conference on database systems for advanced applications, 166\u2013182. Springer, (2022)","DOI":"10.1007\/978-3-031-00126-0_11"},{"key":"5941_CR49","doi-asserted-by":"crossref","unstructured":"Fangye, Wang., Yingxu, Wang., Dongsheng, Li., Hansu, Gu., Tun, Lu., Peng, Zhang., and Ning, Gu.: Cl4ctr: A contrastive learning framework for ctr prediction. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 805\u2013813, (2023)","DOI":"10.1145\/3539597.3570372"},{"issue":"2","key":"5941_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3688570","volume":"43","author":"Y Di","year":"2025","unstructured":"Di, Y., Shi, H., Wang, X., Ma, R., Liu, Y.: Federated recommender system based on diffusion augmentation and guided denoising. ACM Transactions on Information Systems 43(2), 1\u201336 (2025)","journal-title":"ACM Transactions on Information Systems"},{"issue":"1","key":"5941_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3682076","volume":"4","author":"Y Di","year":"2025","unstructured":"Di, Y., Shi, H., Ma, R., Gao, H., Liu, Y., Wang, W.: Fedrl: A reinforcement learning federated recommender system for efficient communication using reinforcement selector and hypernet generator. ACM Transactions on Recommender Systems 4(1), 1\u201331 (2025)","journal-title":"ACM Transactions on Recommender Systems"},{"key":"5941_CR52","unstructured":"Yicheng, Di., Xiaoming, Wang., Hongjian, Shi., Chongsheng, Fan., Rong, Zhou., Ruhui,, Ma., and Yuan, Liu.: Personalized consumer federated recommender system using fine-grained transformation and hybrid information sharing. IEEE Transactions on Consumer Electronics, (2025)"},{"issue":"3","key":"5941_CR53","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1007\/s10115-024-02316-y","volume":"67","author":"Y Di","year":"2025","unstructured":"Di, Y., Shi, H., Wang, Q., Jia, S., Bao, J., Liu, Y.: Federated cross-domain recommendation system based on bias eliminator and personalized extractor. Knowl. Inf. Syst. 67(3), 2935\u20132965 (2025)","journal-title":"Knowl. Inf. Syst."},{"key":"5941_CR54","unstructured":"Chuhan, Wu., Fangzhao, Wu., Yang, Cao., Yongfeng, Huang., and Xing, Xie.: Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925, (2021)"},{"issue":"6","key":"5941_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-021-0236-9","volume":"16","author":"T Wang","year":"2022","unstructured":"Wang, T., Li, J., Huai-Ning, W., Li, C., Snoussi, H., Yang, W.: Reslnet: deep residual lstm network with longer input for action recognition. Front. Comp. Sci. 16(6), 166334 (2022)","journal-title":"Front. Comp. Sci."},{"key":"5941_CR56","unstructured":"Waqar, Ali., Khalid, Umer., Xiangmin, Zhou., and Jie, Shao.: Hidattack: An effective and undetectable model poisoning attack to federated recommenders. IEEE Transactions on Knowledge and Data Engineering, (2024)"},{"key":"5941_CR57","doi-asserted-by":"crossref","unstructured":"Changqin, Huang., Yi,\u00a0Wang., Yunliang, Jiang., Ming, Li., Xiaodi, Huang., Shijin, Wang., Shirui, Pan., and Chuan, Zhou.: Flow2gnn: Flexible two-way flow message passing for enhancing gnns beyond homophily. IEEE Transactions on Cybernetics, (2024)","DOI":"10.1109\/TCYB.2024.3412149"},{"key":"5941_CR58","unstructured":"Chao, Fu., Guannan, Liu., Kun, Yuan., and Junjie, Wu.: Nowhere to h 2 ide: Fraud detection from multi-relation graphs via disentangled homophily and heterophily identification. IEEE Transactions on Knowledge and Data Engineering, (2024)"},{"key":"5941_CR59","doi-asserted-by":"crossref","unstructured":"Khalil, Muhammad., Qinqin, Wang., Diarmuid, O\u2019Reilly-Morgan., Elias, Tragos., Barry, Smyth., Neil, Hurley., James, Geraci., and Aonghus, Lawlor.: Fedfast: Going beyond average for faster training of federated recommender systems. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1234\u20131242, (2020)","DOI":"10.1145\/3394486.3403176"},{"issue":"8","key":"5941_CR60","doi-asserted-by":"publisher","first-page":"10555","DOI":"10.1007\/s12652-022-03709-z","volume":"14","author":"Z Jie","year":"2023","unstructured":"Jie, Z., Chen, S., Lai, J., Arif, M., He, Z.: Personalized federated recommendation system with historical parameter clustering. J. Ambient. Intell. Humaniz. Comput. 14(8), 10555\u201310565 (2023)","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"issue":"5","key":"5941_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3708982","volume":"57","author":"W Ali","year":"2025","unstructured":"Ali, W., Zhou, X., Shao, J.: Privacy-preserved and responsible recommenders: From conventional defense to federated learning and blockchain. ACM Computing Surveys 57(5), 1\u201335 (2025)","journal-title":"ACM Computing Surveys"},{"issue":"1","key":"5941_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3651168","volume":"4","author":"W Ali","year":"2025","unstructured":"Ali, W., Ammad-Ud-Din, M., Zhou, X., Zhang, Y., Shao, J.: Communication-efficient federated neural collaborative filtering with multi-armed bandits. ACM Transactions on Recommender Systems 4(1), 1\u201328 (2025)","journal-title":"ACM Transactions on Recommender Systems"},{"issue":"4","key":"5941_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3633520","volume":"15","author":"W Ali","year":"2024","unstructured":"Ali, W., Kumar, R., Zhou, X., Shao, J.: Responsible recommendation services with blockchain empowered asynchronous federated learning. ACM Transactions on Intelligent Systems and Technology 15(4), 1\u201324 (2024)","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"16","key":"5941_CR64","doi-asserted-by":"publisher","first-page":"27099","DOI":"10.1109\/JIOT.2024.3399074","volume":"11","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Hui, X., Ali, W., Zhou, X., Shao, J.: Bilateral improvement in local personalization and global generalization in federated learning. IEEE Internet Things J. 11(16), 27099\u201327111 (2024)","journal-title":"IEEE Internet Things J."},{"key":"5941_CR65","doi-asserted-by":"crossref","unstructured":"Xiangnan, He., Kuan, Deng., Xiang, Wang., Yan, Li., Yongdong, Zhang., and Meng, Wang.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 639\u2013648, (2020)","DOI":"10.1145\/3397271.3401063"},{"issue":"5","key":"5941_CR66","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNSE.2021.3110677","volume":"9","author":"S Liu","year":"2021","unstructured":"Liu, S., Wang, B., Deng, X., Yang, L.T.: Self-attentive graph convolution network with latent group mining and collaborative filtering for personalized recommendation. IEEE Transactions on Network Science and Engineering 9(5), 3212\u20133221 (2021)","journal-title":"IEEE Transactions on Network Science and Engineering"},{"issue":"6","key":"5941_CR67","doi-asserted-by":"crossref","first-page":"3320","DOI":"10.1109\/TNSE.2023.3272886","volume":"10","author":"W Wang","year":"2023","unstructured":"Wang, W., Quan, Z., Zhao, S., Sun, G., Li, Y., Ben, X., Zhao, J.: User-context collaboration and tensor factorization for gnn-based social recommendation. IEEE Transactions on Network Science and Engineering 10(6), 3320\u20133330 (2023)","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"5941_CR68","unstructured":"Steffen, Rendle., Christoph, Freudenthaler., Zeno, Gantner., and Lars, Schmidt-Thieme.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618, (2012)"},{"key":"5941_CR69","doi-asserted-by":"crossref","unstructured":"Qinbin, Li., Yiqun, Diao., Quan, Chen., and Bingsheng, He.: Federated learning on non-iid data silos: An experimental study. In 2022 IEEE 38th international conference on data engineering (ICDE), pages 965\u2013978. IEEE, (2022)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"5941_CR70","unstructured":"Jinyi, Xu., Zuowei, Zhang., Ze,\u00a0Lin., Yixiang, Chen., Zhe, Liu., and Weiping, Ding.: How to characterize imprecision in multi-view clustering? IEEE Transactions on Emerging Topics in Computational Intelligence, (2025)"},{"key":"5941_CR71","doi-asserted-by":"crossref","unstructured":"Shijie, Zhang., Wei, Yuan., Hongzhi, Yin.: Comprehensive privacy analysis on federated recommender system against attribute inference attacks. IEEE Transactions on Knowledge and Data Engineering, (2023)","DOI":"10.1109\/TKDE.2023.3295601"},{"key":"5941_CR72","doi-asserted-by":"crossref","unstructured":"Ali\u00a0Mamdouh, Elkahky., Yang, Song., Xiaodong, He.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th international conference on world wide web, 278\u2013288, (2015)","DOI":"10.1145\/2736277.2741667"},{"key":"5941_CR73","unstructured":"Kingma, Diederik\u00a0P., and Jimmy, Ba.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, (2014)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05941-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-026-05941-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-026-05941-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T18:39:38Z","timestamp":1775500778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-026-05941-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,6]]},"references-count":73,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["5941"],"URL":"https:\/\/doi.org\/10.1007\/s10586-026-05941-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,6]]},"assertion":[{"value":"15 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interests"}}],"article-number":"248"}}