Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan - Gastani, Anis Hafiza; Naziron, Nur Asyira; Mishan, Mohd Taufik (College of Computing, Informatics, and Mathematics, 2024)
The dietary supplement research identifies challenges in current systems, particularly regarding allergy management within recommendation algorithms for athletes. Existing systems lack robust mechanisms to prioritize and integrate allergy information, raising concerns for athletes with specialized dietary needs. To address this, a tailored recommendation system is proposed, aiming to align with individual athlete preferences, nutritional needs, and prioritize user safety. Developed through collaborative filtering with Singular Value Decomposition (SVD), the system delivers precise suggestions, mitigating risks associated with harmful recommendations. Assessment through black box testing shows commendable ratings for interface functionalities, reinforcing system reliability. Future recommendations include expanding data scraping techniques and exploring advanced collaborative filtering algorithms for enhanced personalization. In conclusion, the proposed system represents a significant advancement in ensuring safe, personalized, and effective supplement recommendations for athletes, fostering trust in their journey towards optimal health and performance.