{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:37:10Z","timestamp":1772257030995,"version":"3.50.1"},"reference-count":170,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T00:00:00Z","timestamp":1521072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2018,5,2]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-06-2017-0196","type":"journal-article","created":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T10:28:15Z","timestamp":1521109695000},"page":"985-1017","source":"Crossref","is-referenced-by-count":83,"title":["Recommender systems"],"prefix":"10.1108","volume":"47","author":[{"given":"Fatemeh","family":"Alyari","sequence":"first","affiliation":[]},{"given":"Nima","family":"Jafari Navimipour","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2018,3,15]]},"reference":[{"issue":"3","key":"key2021041509160929500_ref001","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MIS.2007.58","article-title":"New recommendation techniques for multicriteria rating systems","volume":"22","year":"2007","journal-title":"IEEE Intelligent Systems"},{"issue":"1\/2","key":"key2021041509160929500_ref002","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1023\/A:1009839827683","article-title":"Expert-driven validation of rule-based user models in personalization applications","volume":"5","year":"2001","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"6","key":"key2021041509160929500_ref003","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions","volume":"17","year":"2005","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"key2021041509160929500_ref004","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/1055709.1055714","article-title":"Incorporating contextual information in recommender systems using a multidimensional approach","volume":"23","year":"2005","journal-title":"ACM Transactions on Information Systems (Tois)"},{"issue":"12","key":"key2021041509160929500_ref005","doi-asserted-by":"crossref","first-page":"1730004","DOI":"10.1142\/S0218126617300045","article-title":"Memory designing using quantum-dot cellular automata: Systematic literature review, classification and current trends","volume":"26","year":"2017","journal-title":"Journal of Circuits, Systems and Computers"},{"issue":"1","key":"key2021041509160929500_ref006","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ins.2007.07.024","article-title":"A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem","volume":"178","year":"2008","journal-title":"Information Sciences"},{"issue":"4","key":"key2021041509160929500_ref007","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1016\/j.eswa.2009.09.025","article-title":"Selecting a small number of products for effective user profiling in collaborative filtering","volume":"37","year":"2010","journal-title":"Expert Systems with Applications"},{"key":"key2021041509160929500_ref008","doi-asserted-by":"crossref","unstructured":"Aimeur, E., Brassard, G., Fernandez, J.M. and Onana, F. 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