Smart Food Commendation Scheme Using Machine Learning

Abstract

In the age of data-driven decision-making, intelligent food recommendation systems have emerged as a revolutionary solution to cater to individual preferences, dietary restrictions, and nutritional requirements. This review paper delves into the world of intelligent food recommendation systems powered by machine learning. We explore the evolution of these systems, the underlying technologies, the datasets, and the various machine learning algorithms employed. Additionally, we discuss the potential applications, challenges, and future prospects of this innovative technology in enhancing food choice and overall well-being. The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contain carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same product. Each person has his or her own eating patterns, based on the preferences and dislikes of the user, indicating that personalized diet is important to sustain the success and health of the user. The proposed recommendation method uses a deep learning algorithm and a genetic algorithm to provide the best possible advice.