Atıf İçin Kopyala
Yıldız E., Şen C., Işık E. E.
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, cilt.18, sa.1, ss.571-596, 2023 (SSCI)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
18
Sayı:
1
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Basım Tarihi:
2023
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Doi Numarası:
10.3390/jtaer18010029
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Dergi Adı:
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
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Derginin Tarandığı İndeksler:
Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Directory of Open Access Journals, DIALNET, Civil Engineering Abstracts
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Sayfa Sayıları:
ss.571-596
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Anahtar Kelimeler:
product recommendation system, customer segmentation, association rule mining, RFM analysis, fashion retail industry
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Yıldız Teknik Üniversitesi Adresli:
Evet
Özet
Providing the right products, at the right place and time, according to their customer’s 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’ 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.