Personalized Ontology-based Food Menu Recommender System for Bodybuilders using SWRL Rules - Hakim, Lukman Nur; Baizal, Z. K. A. (Journal of Information System Research (JOSH), 2024)
Bodybuilding requires precise and careful food planning to promote muscle growth and optimize body composition. However, creating personalized meal plans that meet the unique dietary needs of bodybuilders is challenging. This study introduces a customized food recommender system specifically designed for bodybuilders, addressing this problem by utilizing an ontology-based approach combined with Semantic Web Rule Language (SWRL) and a Telegram chatbot. The objective is to provide personalized nutritional guidance that aligns with individual bodybuilding goals. The system employs ontologies to represent key concepts such as user profiles, nutritional needs, and food attributes. SWRL rules generate tailored meal plans based on the user's input, which includes personal information and bodybuilding objectives submitted through the chatbot. The system was evaluated with 15 user profiles, producing 180 food recommendations. The results demonstrated high accuracy, with a precision value of 0.866, a recall value of 1, and an F-Score of 0.928. Although the system effectively delivers personalized nutritional advice, it currently lacks the ability to address specific dietary restrictions. Future work could involve incorporating a wider range of dietary considerations and enhancing the system's applicability. This study highlights the potential of semantic technologies in advancing personalized diet and fitness planning.