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In today's digital era, recommendation systems have become an integral part of supporting consumer purchasing decisions, including in the food and beverage industry. This study aims to develop a product recommendation system for snacks and beverages using the item-based collaborative filtering method. This method was chosen due to its ability to handle large-scale user and product data, as well as its efficiency in providing relevant recommendations based on user consumption patterns. In this study, the system calculates the average user rating and implements   Cosine Similarity to measure the similarity between products, resulting in more accurate recommendations. The system also evaluates the accuracy of recommendations using the Mean Absolute Error (MAE) metric. Based on the results obtained, which is 0.285403 for the average error on 17 items, the developed recommendation system can improve consumers' shopping experience, help them find products that suit their tastes, and support the sales of snacks and beverages products in the market

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

This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.