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All Journal International Journal Software Engineering and Computer Science (IJSECS)
Yulianto Umar Rofi'i
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Computer Science & IT

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E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering Legito; Wattimena, Fegie Yoanti; Yulianto Umar Rofi'i; Munawir
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 2 (2023): AUGUST 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i2.1527

Abstract

This research proposes and implements an e-commerce product recommendation system that combines Case-Based Reasoning (CBR) and K-Means Clustering algorithms. The main aim of this research is to provide more personalized and relevant product recommendations to e-commerce users. The CBR approach leverages users' transaction history to provide customized recommendations, whereas K-Means Clustering groups users with similar preferences increase the relevance of recommendations. This study assesses the effectiveness of the system by conducting a comprehensive evaluation by comparing system recommendations with actual user preferences. The results of this study reveal that the combined approach of CBR and K-Means Clustering can improve the performance of e-commerce product recommendations, ensure the accuracy of recommendations, and produce a more satisfying shopping experience for users. Although there are limitations in terms of the dataset used and the choice of algorithm parameters, this research makes an important contribution in developing a more adaptive and personalized recommendation system for e-commerce platforms.
Co-Authors Legito Munawir Wattimena, Fegie Yoanti