{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:36:56Z","timestamp":1792456616254,"version":"4.5.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62077015"],"award-info":[{"award-number":["62077015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11276-021-02854-1","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T07:03:04Z","timestamp":1641279784000},"page":"1535-1544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Privacy-preserving federated learning framework in multimedia courses recommendation"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1252-4894","authenticated-orcid":false,"given":"YangJie","family":"Qin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"2854_CR1","doi-asserted-by":"crossref","unstructured":"Ali, W., Kumar, R., Deng, Z., Wang, Y., & Shao, J. (2021). A federated learning approach for privacy protection in context-aware recommender systems. The Computer Journal.","DOI":"10.1093\/comjnl\/bxab025"},{"key":"2854_CR2","unstructured":"Ammad-Ud-Din, M., Ivannikova, E., Khan, S. A., Oyomno, W., Fu, Q., Tan, K. E., & Flanagan, A. (2019). Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888."},{"key":"2854_CR3","unstructured":"Bennett, J., Lanning, S., et al. (2007). The netflix prize. In: Proceedings of KDD cup and workshop, 35."},{"key":"2854_CR4","unstructured":"Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Kone\u010dn\u1ef3, J., Mazzocchi, S., McMahan, H. B., et\u00a0al (2019). Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046."},{"key":"2854_CR5","doi-asserted-by":"crossref","unstructured":"Chai, D., Wang, L., Chen, K., & Yang, Q. (2020). Secure federated matrix factorization. IEEE Intelligent Systems.","DOI":"10.1109\/MIS.2020.3014880"},{"key":"2854_CR6","unstructured":"Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604."},{"issue":"12","key":"2854_CR7","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TKDE.2018.2831682","volume":"30","author":"X He","year":"2018","unstructured":"He, X., He, Z., Song, J., Liu, Z., Jiang, Y. G., & Chua, T. S. (2018). Nais: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2354\u20132366.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2854_CR8","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173\u2013182).","DOI":"10.1145\/3038912.3052569"},{"key":"2854_CR9","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939."},{"key":"2854_CR10","doi-asserted-by":"crossref","unstructured":"Jiang, D., Song, Y., Tong, Y., Wu, X., Zhao, W., Xu, Q., & Yang, Q. (2019). Federated topic modeling. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 1071\u20131080).","DOI":"10.1145\/3357384.3357909"},{"key":"2854_CR11","doi-asserted-by":"crossref","unstructured":"Jing, X., & Tang, J. (2017). Guess you like: course recommendation in MOOCs. In Proceedings of the international conference on web intelligence (pp. 783\u2013789).","DOI":"10.1145\/3106426.3106478"},{"key":"2854_CR12","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H. B., Yu, F. X., Richt\u00e1rik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492."},{"key":"2854_CR13","doi-asserted-by":"crossref","unstructured":"Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., & Ma, J. (2017). Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on conference on information and knowledge management (pp. 1419\u20131428).","DOI":"10.1145\/3132847.3132926"},{"key":"2854_CR14","unstructured":"Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019). Fair resource allocation in federated learning. arXiv preprint arXiv:1905.10497."},{"issue":"1","key":"2854_CR15","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/MNET.011.2000263","volume":"35","author":"Y Li","year":"2020","unstructured":"Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., & Yan, Q. (2020). A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network, 35(1), 234\u2013241.","journal-title":"IEEE Network"},{"key":"2854_CR16","doi-asserted-by":"crossref","unstructured":"Liang, F., Pan, W., Ming, Z. (2021). Fedrec++: Lossless federated recommendation with explicit feedback. In Proceedings of the AAAI conference on artificial intelligence (Vol.\u00a035, pp. 4224\u20134231).","DOI":"10.1609\/aaai.v35i5.16546"},{"key":"2854_CR17","doi-asserted-by":"crossref","unstructured":"Lin, G., Liang, F., Pan, W., & Ming, Z. (2020). Fedrec: Federated recommendation with explicit feedback. IEEE Intelligent Systems.","DOI":"10.1109\/MIS.2020.3017205"},{"issue":"1","key":"2854_CR18","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","volume":"7","author":"G Linden","year":"2003","unstructured":"Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76\u201380.","journal-title":"IEEE Internet Computing"},{"key":"2854_CR19","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S.,&\u00a0Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273\u20131282)."},{"key":"2854_CR20","doi-asserted-by":"crossref","unstructured":"Muhammad, K., Wang, Q., O\u2019Reilly-Morgan, D., Tragos, E., Smyth, B., Hurley, N., Geraci, J., & Lawlor, A. (2020). Fedfast: Going beyond average for faster training of federated recommender systems. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1234\u20131242).","DOI":"10.1145\/3394486.3403176"},{"key":"2854_CR21","doi-asserted-by":"crossref","unstructured":"Nishio, T., & Yonetani, R. (2019). Client selection for federated learning with heterogeneous resources in mobile edge. In ICC 2019-2019 IEEE international conference on communications (ICC) (pp. 1\u20137). IEEE.","DOI":"10.1109\/ICC.2019.8761315"},{"key":"2854_CR22","doi-asserted-by":"crossref","unstructured":"Qi, T., Wu, F., Wu, C., Huang, Y., & Xie, X. (2020). Privacy-preserving news recommendation model learning. In Proceedings of the 2020 conference on empirical methods in natural language processing: findings (pp. 1423\u20131432).","DOI":"10.18653\/v1\/2020.findings-emnlp.128"},{"issue":"3","key":"2854_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2168752.2168771","volume":"3","author":"S Rendle","year":"2012","unstructured":"Rendle, S. (2012). Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3), 1\u201322.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"issue":"9","key":"2854_CR24","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","volume":"31","author":"F Sattler","year":"2019","unstructured":"Sattler, F., Wiedemann, S., M\u00fcller, K. R., & Samek, W. (2019). Robust and communication-efficient federated learning from non-IID data. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3400\u20133413.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2854_CR25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762."},{"key":"2854_CR26","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., & Khazaeni, Y. (2020). Federated learning with matched averaging. arXiv preprint arXiv:2002.06440."},{"key":"2854_CR27","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhang, H., Gao, H., Song, S., Yin, Y., Hei, L., Ding, Y., & Barroso, R. J. D. (2021). Preference discovery from wireless social media data in APIS recommendation. Wireless Networks, 1\u201311.","DOI":"10.1007\/s11276-021-02543-z"},{"key":"2854_CR28","doi-asserted-by":"crossref","unstructured":"Yu, J., Luo, G., Xiao, T., Zhong, Q., Wang, Y., Luo, J., Wang, C., Hou, L., Li, J., Liu, Z., et al. (2020). Mooccube: A large-scale data repository for NLP applications in MOOCs. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 3135\u20133142).","DOI":"10.18653\/v1\/2020.acl-main.285"},{"key":"2854_CR29","unstructured":"Yu, J., Wang, C., Luo, G., Hou, L., Li, J., Tang, J., Huang, M., & Liu, Z. (2020). Expanrl: Hierarchical reinforcement learning for course concept expansion in MOOCs. In Proceedings of the 1st conference of the asia-pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing (pp. 770\u2013780)."},{"key":"2854_CR30","doi-asserted-by":"crossref","unstructured":"Yu, L., Liu, L., Pu, C., Gursoy, M. E., & Truex, S. (2019). Differentially private model publishing for deep learning. In 2019 IEEE symposium on security and privacy (SP) (pp. 332\u2013349). IEEE.","DOI":"10.1109\/SP.2019.00019"},{"key":"2854_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., & Sun, J. (2019). Hierarchical reinforcement learning for course recommendation in MOOCs. In Proceedings of the AAAI conference on artificial intelligence (Vol.\u00a033, pp. 435\u2013442).","DOI":"10.1609\/aaai.v33i01.3301435"},{"key":"2854_CR32","doi-asserted-by":"crossref","unstructured":"Zheng, W., Yan, L., Gou, C., & Wang, F. Y. (2020). Federated meta-learning for fraudulent credit card detection. In Proceedings of the twenty-ninth international joint conference on artificial intelligence (IJCAI-20).","DOI":"10.24963\/ijcai.2020\/642"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02854-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-021-02854-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02854-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T15:09:17Z","timestamp":1682608157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-021-02854-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,4]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["2854"],"URL":"https:\/\/doi.org\/10.1007\/s11276-021-02854-1","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,4]]},"assertion":[{"value":"9 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}