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Comparing Multiple Ensemble Classifiers for E-commerce Recommendation System

Comparing Multiple Ensemble Classifiers for E-commerce Recommendation System
	© 2025 by IJETT Journal
	Volume-73 Issue-1
	Year of Publication : 2025
	Author : Ignatius Michael Dinata, Vincentius Loanka Sinaga, Antoni Wibowo
	DOI : 10.14445/22315381/IJETT-V73I1P121

How to Cite?
Ignatius Michael Dinata, Vincentius Loanka Sinaga, Antoni Wibowo, "Comparing Multiple Ensemble Classifiers for E-commerce Recommendation System," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 250-254, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P121

Abstract
E-commerce recommendation systems face challenges with data sparsity, which impacts the accuracy of user engagement and product recommendations. This research evaluates the performance of multiple Machine Learning classifiers, including Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM), with and without the use of the Synthetic Minority Over-sampling Technique (SMOTE). The results indicate that XGBoost with SMOTE achieves the highest performance across all evaluation metrics (accuracy, precision, recall, and F1-score), scoring 0.97 in each metric. Random Forest also performs well, achieving 0.95 for all metrics, while KNN scores moderately at 0.83. SVM shows the lowest performance, with an accuracy of 0.59 and an F1-score of 0.51. These findings highlight the robustness of XGBoost combined with SMOTE in handling imbalanced data and improving prediction accuracy in e-commerce recommendation systems, offering valuable insights for researchers and practitioners in this domain.

Keywords
XGBoost, KNN, Random Forest, SVM, E-Commerce.

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