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All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Siti Aminah
Faculty of Science Technology and Health Universitas Nahdlatul Ulama South Kalimantan
Computer Science & IT Electrical & Electronics Engineering Engineering

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Evaluating Recommendation Accuracy in E-Commerce: A Comparison Between Content-Based and Collaborative Filtering Methods Muhammad Fajrian Noor; Sofyar; Siti Aminah
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i1.465

Abstract

Product recommendation systems play an important role in helping users find products that match their interests and needs on e-commerce platforms. This research aims to compare the effectiveness of two popular methods in recommendation systems, namely Content-Based Filtering and Collaborative Filtering. The research method used is quantitative with data collection through questionnaires which are then analysed using evaluation metrics such as precision, recall, and F1-score to measure the accuracy level of each method. The results show that Content-Based Filtering provides more accurate recommendations than Collaborative Filtering in the context of this research. This finding indicates that product characteristics relevant to user preferences have a more dominant influence in generating appropriate recommendations, compared to other user preferences. This research makes an important contribution to the development of a more effective recommendation system to improve user experience in finding relevant products on e-commerce platforms. Thus, the results of this study can serve as a reference for recommendation system developers in choosing the most suitable method to improve user satisfaction and system performance.
Co-Authors Muhammad Fajrian Noor