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
