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.
CITATION STYLE
Legito, Wattimena, F. Y., Yulianto Umar Rofi’i, & Munawir. (2023). E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering. International Journal Software Engineering and Computer Science (IJSECS), 3(2), 162–173. https://doi.org/10.35870/ijsecs.v3i2.1527
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