{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:29:45Z","timestamp":1777508985250,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,8,30]],"date-time":"2020-08-30T00:00:00Z","timestamp":1598745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users\u2019 personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.<\/jats:p>","DOI":"10.3390\/ijgi9090519","type":"journal-article","created":{"date-parts":[[2020,8,30]],"date-time":"2020-08-30T22:00:17Z","timestamp":1598824817000},"page":"519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Social\u2013Aware Recommender System Based on User\u2019s Personal Smart Devices"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-1938","authenticated-orcid":false,"given":"Soroush","family":"Ojagh","sequence":"first","affiliation":[{"name":"Dept. of GIS, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19967-15433, Iran"},{"name":"Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 4V8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0893-8197","authenticated-orcid":false,"given":"Mohammad Reza","family":"Malek","sequence":"additional","affiliation":[{"name":"Dept. of GIS, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19967-15433, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1480-6382","authenticated-orcid":false,"given":"Sara","family":"Saeedi","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 4V8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.comcom.2019.03.009","article-title":"Social Internet of Things (SIoT): Foundations, thrust areas, systematic review and future directions","volume":"139","author":"Roopa","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Malek, M.R., and Frank, A.U. (2006). A mobile computing approach for navigation purposes. 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