Fuzzy Inference System Based Intelligent Food Recommender System

The personalized suggestions using the intelligent methods are becoming promising ways to change eating habits to-ward more desirable diets. This has necessitated the requirements of intelligent food recommender system (FRS). This paper has designed a FRS using the Fuzzy Inference System (FIS) to suggest the food preference (FP). This is achieved using the different system blocks such as Rule Base, Database, Decision-making Unit, Fuzzification Interface Unit (FIU), and Defuzzification Interface Unit (DIU). The FP is decided by processing the input variables such as Age, Income and Calorific values. This is achieved with an accuracy of higher than 95%. Efficacy of proposed FRS is established by comparing the performance with the rule based FRS reported in literature. Study is performed using the coding environment of MATLAB.