Food Recommended System Using AI Techniques

Author

Dr. Bernard Edward Swamidoss S

Abstract

 The rapid growth of online food ordering and digital restaurant services has created a need for intelligent systems that can help customers select suitable food items from a large number of available choices. Traditional food selection methods generally depend on manually browsing menus, customer preferences, or basic search and filtering techniques. These approaches may not provide personalized recommendations based on individual interests. This paper presents an Artificial Intelligence-based Food Recommendation System designed to recommend food items according to customer preferences, previous selections, dietary requirements, food categories, and item characteristics. The proposed system collects relevant user information such as preferred cuisine, favorite food categories, previous orders, ratings, and dietary preferences and processes this information using recommendation techniques. Machine learning methods can be used to identify similarities between users and food items and generate personalized recommendations. The system can also consider factors such as price range, nutritional preferences, popularity, and previous feedback to improve recommendation quality. A web-based interface allows users to view recommended food items and select suitable meals conveniently. The proposed system can improve the food selection experience, reduce the time required to search through menus, and support restaurants in providing personalized services to customers. The system can be implemented using Python, Flask, machine learning libraries, MySQL, HTML, CSS, and Bootstrap.

Keywords

Artificial Intelligence, Food Recommendation, Machine Learning, Personalized Recommendation, Restaurant Management, User Preference, Collaborative Filtering, Content-Based Filtering.

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