Low-Sugar Diet Recommendations for Bangrajan Muay Thai 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 one low in sugar foods. 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 programme through food recommendations tailored to their individual behaviours 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 personalised nutrition for combat sports athletes to support athlete performance.