Low-Sugar Diet Recommendations for Bangrajanmuaythai Boxing Athletes Using Collaborative Filtering

Abstract

Adjusting diet patterns according to nutritional requirements, training intensity, and an athlete's physical condition is often a challenge in implementing a healthy diet, particularly a low-sugar food diet. This study aims to develop an artificial intelligence (AI)-based recommendation system that can help boxing and Muay Thai athletes in implementing a more targeted diet program through food recommendations tailored to their individual behaviors and nutritional needs. The methods used are collaborative filtering with a nutrition scoring approach, athlete preference analysis, and dynamic nutrition planning. The results show that the developed system, namely the Smart Nutrition System, is able to provide recommendations based on similarities among athletes’ preferences and nutritional requirements, thus supporting more effective decision-making in managing athlete diet patterns. Furthermore, the Smart Nutrition System also has the potential to evolve into an “athlete intelligence nutrition platform" that supports the implementation of personalized nutrition for combat sports athletes to support athlete performance.