{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:52:31Z","timestamp":1787028751583,"version":"build-2736575974"},"reference-count":81,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T00:00:00Z","timestamp":1678492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Providing the right products, at the right place and time, according to their customer\u2019s preferences, is a problem-seeking solution, especially for companies operating in the retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. The proposed approach consists of the following steps: (1) customer segmentation; (2) adding the location dimension and determining the association rules; (3) the creation of product recommendations. We used the RFM technique for customer segmentation and the k-means clustering algorithm to create customer segments with customer-based RFM values. Then, the Apriori algorithm, one of the association rule mining algorithms, is used to create accurate rules. In this way, cluster-based association rules are created. Finally, product recommendations are presented with a rule-based heuristic algorithm. This is the first system that considers customers\u2019 demographic data in the fashion retail industry in the literature. Furthermore, the customer location information is used as a parameter for the first time for the clustering phase of a fashion retail product RS. The proposed systematic approach is aimed at producing hyper-personalized product recommendations for customers. The proposed system is implemented on real-world e-commerce data and compared with the current RSs used according to well-known metrics and the average sales information. The results show that the proposed system provides better values.<\/jats:p>","DOI":"10.3390\/jtaer18010029","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T06:52:13Z","timestamp":1678690333000},"page":"571-596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail Industry"],"prefix":"10.3390","volume":"18","author":[{"given":"Emre","family":"Y\u0131ld\u0131z","sequence":"first","affiliation":[{"name":"Department of IT System Development, Colin\u2019s Erk Marketing and Clothing Industry and Trade Inc., \u0130stanbul 34396, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5386-0803","authenticated-orcid":false,"given":"Ceyda","family":"G\u00fcng\u00f6r \u015een","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Yildiz Technical University, \u0130stanbul 34220, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9180-0243","authenticated-orcid":false,"given":"Ey\u00fcp Ensar","family":"I\u015f\u0131k","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Yildiz Technical University, \u0130stanbul 34220, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100016","DOI":"10.1016\/j.multra.2022.100016","article-title":"A personalized recommendation system for multi-modal transportation systems","volume":"1","author":"Wu","year":"2022","journal-title":"Multimodal Transp."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115694","DOI":"10.1109\/ACCESS.2020.3002803","article-title":"A Systematic Study on the Recommender Systems in the E-Commerce","volume":"8","author":"Alamdari","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., and Kashef, R. 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