<![CDATA[The rapid growth of e-commerce platforms demands recommendation systems capable of delivering relevant and personalized product suggestions to users. Collaborative filtering has emerged as a dominant approach due to its ability to leverage user interaction patterns without relying on explicit product content information. This study aims to examine and design an e-commerce product recommendation system using collaborative filtering by integrating empirical findings from previous studies. A quantitative approach based on recommendation system modeling was employed, utilizing user interaction data such as purchase history, ratings, and browsing behavior. The results indicate that collaborative filtering significantly improves recommendation accuracy, user engagement, and sales potential, despite challenges such as cold start and data sparsity. The integration of hybrid models, machine learning techniques, and neural networks has proven effective in addressing these limitations. This study contributes both conceptually and practically to the development of adaptive and sustainable e-commerce recommendation systems.]]>
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