A naïve bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information

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dc.authorid0000-0001-5988-8882en_US
dc.contributor.authorGültekin, Günay
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2022-02-02T13:51:01Z
dc.date.available2022-02-02T13:51:01Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn recent years, researchers have been trying to create recommender systems. There are many diferent recommender systems. Point of Interest (POI) is a new type of recommender systems that focus on personalized and context-aware recommendations to improve user experience. Recommender systems use diferent types of recommendation methods to obtain information on POI. In this research paper, we introduced a Naïve Bayes Prediction Model based on Bayesian Theory for POI recommendation. Then, we used the Brightkite dataset to make predictions on POI recommendation and compared it with the other two diferent recommendation methods. Experimental results confrm that our proposed method outperforms on Location-based POI recommendation.en_US
dc.identifier.citationGültekin, G., & Bayat, O. (2022). A Naïve Bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information. Multimedia Tools and Applications, 1-22.en_US
dc.identifier.endpage22en_US
dc.identifier.scopus2-s2.0-85123240803
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2235
dc.identifier.wosWOS:000744767900003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.isversionof10.1007/s11042-021-11676-4en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRecommendation Algorithmsen_US
dc.subjectCollaborative Flteringen_US
dc.subjectFactorizationen_US
dc.subjectBig Data Analysisen_US
dc.subjectLocation-Based Social Networksen_US
dc.subjectNaïve Bayes Theoremen_US
dc.titleA naïve bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information
dc.typeArticle

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