RESTAURANT RECOMMENDATION WEB APP BASED ON USER PREFERENCES

Abstract

In the age of digital innovation and culinary diversity, dining enthusiasts are constantly seeking personalised restaurant recommendations that align with their unique preferences and tastes. The "Restaurant Recommendation Web App" represents a comprehensive solution to address this need by harnessing the power of data-driven algorithms and user-generated content. It’s a platform that not only revolutionises the way users discover dining establishments but also facilitates food enthusiasts, bloggers, and critics in sharing their experiences. The core objective of this FYP is to design and construct a user-friendly web application that seamlessly recommends restaurants to users based on several key criteria, including budget constraints, ambience preferences, cuisine types, reviews and more. Leveraging content-based filtering and Sentiment Analysis, the web app provides personalised recommendations that enhance the dining experience by aligning with each user's individual preferences. The project's technical architecture consists of user profiles, restaurant data, and recommendation algorithms. Furthermore, the Restaurant Recommendation Web App serves as a dynamic platform for the food community, allowing food enthusiasts, bloggers, and critics to contribute valuable insights through reviews, blogs, and ratings. By fostering a food community, the app not only enhances its recommendation engine but also creates a vibrant space for foodies. In conclusion, the Restaurant Recommendation Web App represents an innovative and multifaceted solution for restaurant discovery and culinary exploration. By combining the power of data-driven recommendations with a dynamic community of food enthusiasts, the app aims to redefine how users discover and engage with the vibrant world of dining