<![CDATA[The rapid development of digital technology has significantly transformed commercial activities, particularly through the emergence of marketplaces as platforms for online transactions. The vast number of products available in a marketplace often creates difficulties for users in finding items that suit their needs and preferences. To address this challenge, a recommendation system is required to provide personalized and relevant product suggestions. This study discusses the implementation of a product recommendation system in a marketplace using the Collaborative Filtering method. This method works by leveraging information from users’ previous behavior, such as purchase history, ratings, and similarity of preferences with other users, to generate more accurate product recommendations. The Collaborative Filtering approach has proven effective in identifying user preference patterns based on relationships between users as well as between items. This study employs user interaction data such as ratings and shopping activities as the processing foundation. The process involves data collection, preprocessing, calculation of similarity between users or products, and generating recommendation lists. The results indicate that this method enhances the shopping experience by providing relevant product suggestions tailored to user interests, thereby increasing customer satisfaction and potentially improving sales performance in the marketplace. Thus, the application of a Collaborative Filtering-based recommendation system not only simplifies product discovery for users but also offers strategic advantages for marketplace operators in digital business competition]]>
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