{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:37:44Z","timestamp":1760060264595,"version":"build-2065373602"},"reference-count":79,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Network"],"abstract":"<jats:p>Cloud computing has increasingly adopted multi-tenant infrastructures to enhance cost efficiency and resource utilization by enabling the shared use of computational resources. However, this shared model introduces several security and privacy concerns, including unauthorized access, data redundancy, and susceptibility to malicious activities. In such environments, the effectiveness of cloud-based recommendation systems largely depends on the trustworthiness of participating nodes. Traditional collaborative filtering techniques often suffer from limitations such as data sparsity and the cold-start problem, which significantly degrade rating prediction accuracy. To address these challenges, this study proposes a Trusted Graph-Based Collaborative Filtering Recommender System (TGBCF). The model integrates graph-based trust relationships with collaborative filtering to construct a trust-aware user network capable of generating reliable service recommendations. Each node\u2019s reliability is quantitatively assessed using a trust metric, thereby improving both the accuracy and robustness of the recommendation process. Simulation results show that TGBCF achieves a rating prediction accuracy of 93%, outperforming the baseline collaborative filtering approach (82%). Moreover, the model reduces the influence of malicious nodes by 40\u201360%, demonstrating its applicability in dynamic and security-sensitive cloud service environments.<\/jats:p>","DOI":"10.3390\/network5030030","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T13:30:38Z","timestamp":1755091838000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Service Recommendation Model in Cloud Environment Based on Trusted Graph-Based Collaborative Filtering Recommender System"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0419-4530","authenticated-orcid":false,"given":"Urvashi Rahul","family":"Saxena","sequence":"first","affiliation":[{"name":"School of IT and Engineering (SITE), Melbourne Institute of Technology (MIT), 288 La Trobe Street, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8789-5932","authenticated-orcid":false,"given":"Yogita","family":"Khatri","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad 121004, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-2148","authenticated-orcid":false,"given":"Rajan","family":"Kadel","sequence":"additional","affiliation":[{"name":"School of IT and Engineering (SITE), Melbourne Institute of Technology (MIT), 288 La Trobe Street, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9961-1001","authenticated-orcid":false,"given":"Samar","family":"Shailendra","sequence":"additional","affiliation":[{"name":"School of IT and Engineering (SITE), Melbourne Institute of Technology (MIT), 288 La Trobe Street, Melbourne, VIC 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10059","DOI":"10.1016\/j.eswa.2012.02.038","article-title":"A Literature Review and Classification of Recommender Systems Research","volume":"39","author":"Park","year":"2012","journal-title":"Expert Syst. 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