A Personalized Meal Recommendation Approach For Smart Refrigerator.

This paper presents a food recommendation system that utilizes the multiple regression formula to suggest suitable dishes and meals. The system incorporates several independent variables, including ingredient availability (consisting of ingredient weights and expiration weights), user ratings, user health conditions, and nutritional responsiveness. The dependent variable is the appropriateness of the suggested dish and meal choices for the users. By employing the multiple regression formula, our system aims to understand the relationships between these variables and develop an effective method for providing personalized food and meal recommendations. The results contribute to the field of personalized food recommendation and provide insights for enhancing user satisfaction and promoting healthy eating habits.