Abstract:
"The restaurant and food industry has seen a proliferation of recommendation systems, which
offers an opportunity for personalizing restaurant and food suggestions based on the preference
of the users. However, there is a domain research gap in considering user allergies, which can
adversely affect the quality and relevancy of recommendations.
In this research project, the author proposes a hybrid recommendation system that is a
combination of content-based filtering and collaborative filtering techniques to provide more
accurate recommendations for users with dietary restrictions. The system is based on the
analysis of user preferences and allergies and utilizes machine learning algorithms to
recommend the preferred restaurant and food options to the user.
‘Foodie’s Choice’ is a web application that is capable of presenting personalized
recommendations to the user based on their preferences and allergies using the research model."
