E-commerce Product Categorization Using Big Data Analytics
| dc.contributor.author | Usluoğlu, Sedat | |
| dc.contributor.author | Kılınç, Deniz | |
| dc.contributor.author | Bozyiğit, Fatma | |
| dc.date.accessioned | 2025-03-21T07:38:23Z | |
| dc.date.available | 2025-03-21T07:38:23Z | |
| dc.date.issued | 2021 | |
| dc.department | İzmir Bakırçay Üniversitesi | |
| dc.description.abstract | E-commerce platforms need to have a well-managed online product catalog to make products easily accessible. However, the organization of catalog and categorization of products can be time-consuming due to the large volume of product data in e-commerce. In this direction, our study aims to develop an accurate categorization of product data with the adoption of big data analytics. Accordingly, various machine learning algorithms (Support Vector Machine, Naive Bayes, and Stochastic Gradient Descent) were utilized to organize online catalogs from Spark MLLib. Performed classifiers were trained and tested on product catalog data collected from a fashion retailer in Turkey, Boyner Group, which combines cutting-edge digital services with a vast network of exciting stores. | |
| dc.description.sponsorship | İzmir Bakırçay Üniversitesi | |
| dc.identifier.issn | 2757-9778 | |
| dc.identifier.issue | 2 | |
| dc.identifier.startpage | 8-Jan | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14034/2768 | |
| dc.identifier.volume | 1 | |
| dc.language.iso | en | |
| dc.relation.ispartof | Artificial Intelligence Theory and Applications | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_DergiPark_20250319 | |
| dc.subject | e-commerce platforms | |
| dc.subject | product categorization | |
| dc.subject | machine learning | |
| dc.subject | big data analytic tools | |
| dc.title | E-commerce Product Categorization Using Big Data Analytics | |
| dc.type | Article |
