A Diet Recommendation System using TF-IDF and Extra Trees Algorithm

Abstract

Across the globe, there is a growing emphasis on health and lifestyle choices. However, refraining from unhealthy foods and staying active are not enough; maintaining a well-balanced diet is also essential. Recently, recommendation systems have focused on promoting healthy eating habits, tailoring suggestions for balanced diets based on some parameters like age, gender, height, weight, age, BMI and BMR. Pairing a nutritious diet with regular physical activity can aid in reaching and sustaining a healthy weight, reducing the likelihood of chronic ailments such as heart disease, and enhancing overall well-being. The present paper introduces a novel approach for constructing dietary recommendations with optimized calorie intake, using content-based filtering with the TF-IDF statistical method and machine learning with the Extra Trees algorithm. This approach can generate a dynamic diet based on the calories a person burns and other parameters including the current Body Mass Index (BMI) and BMR (Basal Metabolic Rate). The proposed approach has been tested on a new realworld diet dataset, showcasing its effectiveness in providing diverse and accurate diet recommendations compared to another content-based filtering method and other machine learning algorithms.