Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey

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dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorKhan, Nasrullah
dc.contributor.authorMa, Zongmin
dc.contributor.authorUllah, Aman
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-09-05T11:46:12Z
dc.date.available2023-09-05T11:46:12Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionAcknowledgements The work was supported in part by the Basic Research Program of Jiangsu Province (BK20191274) and the National Natural Science Foundation of China (62176121 and 61772269) .en_US
dc.description.abstractRecommender Systems (RS) are established to deal with the preferences of users to enhance their experience and interest in innumerable online applications by streamlining the stress persuaded by the reception of excessive information through the recommendation methods. Although researches have put a lot of efforts in making recommendation processes accurate, specific, and personalized; different issues like cold start, data sparsity or gray sheep etc., still pop up in one or the other form of challenges. Recently, exploitation of Knowledge Graph (KG)-based data as Side Information in recommendation methods has revealed as a sign of resolution to the corresponding challenges; and thus, acquired incredible focus, applicability, and popularity. The incorporation of KG in recommendation has not only effectively alleviated the contrasting challenges, but also has provided specific, accurate, personalized and explainable recommendations about the target items to the end users. In this paper, we explore well-known RSs, popular knowledge repositories, benchmark datasets, recommendation methods, and future research dimensions about the current research. Intuitively, we investigate recommendation methods and associated datasets with respect to the corresponding application scenarios in a categorical way.en_US
dc.description.sponsorshipBasic Research Program of Jiangsu Province [BK20191274]; National Natural Science Foundation of China [62176121, 61772269]en_US
dc.identifier.citationKhan, N., Ma, Z., Ullah, A., & Polat, K. (2022). Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey. Expert Systems with Applications, 206, 117737.en_US
dc.identifier.doi10.1016/j.eswa.2022.117737
dc.identifier.endpage28en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85134533212en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2022.117737
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11662
dc.identifier.volume206en_US
dc.identifier.wosWOS:000835136700004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryDerleme - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCategorizationen_US
dc.subjectKnowledge Graphen_US
dc.subjectSide Informationen_US
dc.subjectLarge-Scaleen_US
dc.subjectDbpediaen_US
dc.subjectSystemsen_US
dc.titleCategorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive surveyen_US
dc.typeReview Articleen_US

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