Personalized Café Menu Recommendation Using Hybrid Collaborative and Content-Based Filtering Based on Location and User Interaction - amirullah; fika adilah (PT. Literasi Teknologi Informatika, 2026)
The culinary industry, particularly cafes, has experienced rapid growth with the increasing number of cafes in various regions. Intense competition has driven café owners to innovate in attracting and retaining customers. A personalized menu recommendation system has become an effective solution to provide relevant services for each customer. This study employs a hybrid method that combines Collaborative Filtering and Content-Based Filtering to address this issue. Three cafes are included in this study: Station Coffee Premium in Kuta Blang, Ocean Coffee in Kampung Jawa Lama, and Bagi-Bagi Coffee in Lancang Garam. The research utilizes three main parameters: menu data, order history data, and café location data. By using these three parameters, the system will display menu recommendations at the cafes according to the customer's preferences. The research results indicate that the recommendation system performed well in black box testing, achieving a 100% success rate in all tested scenarios. Furthermore, method testing shows that the Content-Based Filtering method provides consistent results with stable precision, recall, and MAP across various scenarios. However, the Hybrid Filtering method proved to be the most accurate and relevant, combining the strengths of Content-Based Filtering and Collaborative Filtering to deliver menu recommendations that align with customer preferences based on their order history and location.