Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis

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dc.contributor.authorTombuş, Ayşe Cilacı
dc.contributor.authorEroğlu, Ergin
dc.contributor.authorAltun, İbrahim Halil
dc.date.accessioned2026-02-08T15:03:28Z
dc.date.available2026-02-08T15:03:28Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractRecommender systems in the industrial sector are experiencing a growing application within e-commerce platforms, focusing on tailoring customer shopping experiences. This trend has led to increased customer satisfaction and enhanced sales outcomes for businesses operating in this domain. Despite the widespread prevalence of e-commerce globally, there exists a noticeable gap in the empirical assessment of recommender system performance for business objectives, particularly in the context of utilizing data mining methodologies and big data analytics. This research aims to address this gap by scrutinizing authentic global e-commerce data that spans diverse countries, industries, and scales. The primary objective is to ascertain the impact of recommender systems, measured in terms of contribution rate, click-through rate, conversion rate, and revenue, by leveraging advanced big data analytics and data mining techniques. The study utilizes average values derived from an extensive dataset comprising 200 distinct e-commerce websites, representing a spectrum of 25 countries distributed across five different regions. Notably, this research represents a pioneering initiative in the literature as it harnesses and analyzes empirical data on such a comprehensive scale derived from various global e-commerce platforms.
dc.description.sponsorshipSegmentify Yazılım A.Ş.
dc.identifier.doi10.38088/jise.1308353
dc.identifier.endpage265
dc.identifier.issn2602-4217
dc.identifier.issue2
dc.identifier.startpage251
dc.identifier.urihttps://doi.org/10.38088/jise.1308353
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4141
dc.identifier.volume8
dc.language.isoen
dc.publisherBursa Teknik Üniversitesi
dc.relation.ispartofJournal of Innovative Science and Engineering
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectSoftware Engineering
dc.subjectYazılım Mühendisliği [EN] Industrial Engineering
dc.subjectEndüstri Mühendisliği
dc.titleImpact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis
dc.typeArticle

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