An Ontology based product recommendation system for next generation e-retail

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dc.authorid0000-0001-8224-6319en_US
dc.authorid0000-0002-1030-3545en_US
dc.authorscopusid12769979200en_US
dc.authorscopusid24491277000en_US
dc.authorwosid-en_US
dc.authorwosidJAN-6981-2023en_US
dc.contributor.authorTiryaki, Ali Murat
dc.contributor.authorYücebaş, Sait Can
dc.date.accessioned2024-01-22T08:23:21Z
dc.date.available2024-01-22T08:23:21Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.en_US
dc.identifier.citationTiryaki, A. M., & Yücebaş, S. C. (2023). An Ontology based product recommendation system for next generation e-retail. Journal of Organizational Computing and Electronic Commerce, 33(1–2), 1–21. https://doi.org/10.1080/10919392.2023.2226542en_US
dc.identifier.doi10.1080/10919392.2023.2226542
dc.identifier.issn1091-9392
dc.identifier.issn1532-7744
dc.identifier.scopus2-s2.0-85164533264
dc.identifier.urihttps://doi.org/10.1080/10919392.2023.2226542
dc.identifier.urihttps://hdl.handle.net/20.500.12428/5289
dc.identifier.wosWOS:001019818300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTiryaki, Ali Murat
dc.institutionauthorYücebaş, Sait Can
dc.language.isoen
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofJournal of Organizational Computing and Electronic Commerceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/7141011
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision treeen_US
dc.subjecte-Commerceen_US
dc.subjectMachine learningen_US
dc.subjectOntology based machine learningen_US
dc.subjectOntology engineeringen_US
dc.subjectPersonal recommendationen_US
dc.subjectSemantic interpretationen_US
dc.titleAn Ontology based product recommendation system for next generation e-retail
dc.typeArticle

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