{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T22:40:28Z","timestamp":1772664028949,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61832001"],"award-info":[{"award-number":["61832001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672133"],"award-info":[{"award-number":["61672133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2019YFG0535"],"award-info":[{"award-number":["2019YFG0535"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users\u2019 privacy. Instead of crawling users\u2019 personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users\u2019 privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab025","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T20:10:36Z","timestamp":1615407036000},"page":"1016-1027","source":"Crossref","is-referenced-by-count":49,"title":["A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems"],"prefix":"10.1093","volume":"64","author":[{"given":"Waqar","family":"Ali","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Faculty of Information Technology, The University of Lahore, Lahore 54000, Pakistan"}]},{"given":"Rajesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhiyi","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yansong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jie","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Sichuan Artificial Intelligence Research Institute, Yibin 644000, China"}]}],"member":"286","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"2021103114302068900_ref1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/SP.2008.33","volume-title":"2008 IEEE Symposium on Security and Privacy (S&P 2008)","author":"Narayanan","year":"2008"},{"key":"2021103114302068900_ref2","first-page":"1","volume-title":"International Joint Conference on Neural Networks, IJCNN 2019","author":"Nguyen","year":"2019"},{"key":"2021103114302068900_ref3","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.1007\/s10489-020-01656-w","article-title":"Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data","volume":"50","author":"Majeed","year":"2020","journal-title":"Appl. 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