Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis
Recommender 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...
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| Format: | Article |
| Language: | English |
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Bursa Technical University
2024-12-01
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| Series: | Journal of Innovative Science and Engineering |
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| Online Access: | http://jise.btu.edu.tr/en/download/article-file/3182260 |
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| _version_ | 1832586919677001728 |
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| author | Ayşe CILACI TOMBUS Ergin EROGLU İbrahim Halil ALTUN |
| author_facet | Ayşe CILACI TOMBUS Ergin EROGLU İbrahim Halil ALTUN |
| author_sort | Ayşe CILACI TOMBUS |
| collection | DOAJ |
| description | Recommender 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. |
| format | Article |
| id | doaj-art-3dabcad4c21346c1a0ff54528afb0c9f |
| institution | Kabale University |
| issn | 2602-4217 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Bursa Technical University |
| record_format | Article |
| series | Journal of Innovative Science and Engineering |
| spelling | doaj-art-3dabcad4c21346c1a0ff54528afb0c9f2025-01-24T19:35:02ZengBursa Technical UniversityJournal of Innovative Science and Engineering2602-42172024-12-018225126510.38088/jise.1308353Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical AnalysisAyşe CILACI TOMBUS 0https://orcid.org/0000-0002-0556-7482 Ergin EROGLU1https://orcid.org/0009-0002-0627-4048İbrahim Halil ALTUN2https://orcid.org/0009-0002-0310-7930GEBZE TECHNICAL UNIVERSITY, FACULTY OF ENGINEERINGSegmentify Software Inc.Segmentify Software Inc.Recommender 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.http://jise.btu.edu.tr/en/download/article-file/3182260data miningdata analysise-commercerecommender systemsbig data |
| spellingShingle | Ayşe CILACI TOMBUS Ergin EROGLU İbrahim Halil ALTUN Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis Journal of Innovative Science and Engineering data mining data analysis e-commerce recommender systems big data |
| title | Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis |
| title_full | Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis |
| title_fullStr | Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis |
| title_full_unstemmed | Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis |
| title_short | Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis |
| title_sort | impact of recommender systems in e commerce a worldwide empirical analysis |
| topic | data mining data analysis e-commerce recommender systems big data |
| url | http://jise.btu.edu.tr/en/download/article-file/3182260 |
| work_keys_str_mv | AT aysecilacitombus impactofrecommendersystemsinecommerceaworldwideempiricalanalysis AT ergineroglu impactofrecommendersystemsinecommerceaworldwideempiricalanalysis AT ibrahimhalilaltun impactofrecommendersystemsinecommerceaworldwideempiricalanalysis |