Application of Content-Based Filtering Method Using Cosine Similarity in Restaurant Selection Recommendation System

This research focuses on developing a restaurant recommender system designed to assist users in selecting restaurants based on preferences such as cuisine type and proximity, thereby enhancing the dining experience. The system employs a content-based filtering approach combined with the Cosine Similarity algorithm to calculate similarity values between restaurant addresses and categories, ensuring personalized and accurate recommendations. Data for the system was collected from TripAdvisor and Google Maps using a web scraping method, resulting in a comprehensive dataset that reflects a wide variety of dining options. An experiment involving 30 respondents was conducted to evaluate the system's performance under real-world conditions. The results demonstrated an accuracy rate of 88%, indicating that the recommender system effectively delivers highly relevant restaurant suggestions to users. These findings suggest that the system can serve as a valuable tool for culinary tourists and local residents, simplifying the process of discovering new dining experiences and aligning them with individual preferences.