A Recommendation System for Food Tourism

By making personalized restaurant recommendations based on visitors interests and needs, the Restaurant Recommender for Food Tourism project seeks to improve tourists gastronomic experiences. A trustworthy and effective system is required to help travelers choose the best restaurants that suit their tastes and dietary requirements due to the rising popularity of culinary tourism, which encourages visitors to sample local cuisines and dining venues. In order to build a strong recommendation system, this project uses machine learning methods and user data analysis. The recommender system creates personalized recommendations that match the person’s specific interests by considering variables like cuisine type, price range, location, review counts, and user reviews. The system also has a user-friendly interface that enables users to enter preferences with ease.