<![CDATA[The development of a culinary recommendation system for the city of Solo through the utilization of collaborative filtering techniques is outlined in this investigation. This approach was selected based on its capacity to enhance user preferences through comparison with individuals sharing similar values. The dataset utilized in this study comprises feedback from users and evaluations of restaurants obtained from Google Maps. Collaborative filtering methodologies, particularly those centered on users, are employed to construct recommendation frameworks that offer personalized and pertinent advice to users. The initial phase of system development involved aggregating data from Google rankings and implementing a recommendation model. Findings from this research indicate that the system effectively delivers precise culinary suggestions, thereby facilitating users in discovering suitable dining options in Solo. The anticipated outcome of implementing this system is to bolster the promotion of local culinary specialties, enhance the culinary tourism experience, and contribute positively to the local economy of Solo. This study offers insights into the application of collaborative filtering and underscores the significance of recommendation technologies in enhancing the quality of culinary experiences and user satisfaction.]]>
