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Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan

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 d...

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Main Authors: Gastani, Anis Hafiza, Naziron, Nur Asyira, Mishan, Mohd Taufik
Format: Article
Language:English
Published: College of Computing, Informatics, and Mathematics 2024
Subjects:
Integer programming
Online Access:https://ir.uitm.edu.my/id/eprint/106073/1/106073.pdf
https://ir.uitm.edu.my/id/eprint/106073/
https://fskmjebat.uitm.edu.my/pcmj/
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spelling my.uitm.ir.1060732025-02-26T17:08:22Z https://ir.uitm.edu.my/id/eprint/106073/ 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 Integer programming 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. College of Computing, Informatics, and Mathematics 2024-10 Article NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/106073/1/106073.pdf Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan. (2024) Progress in Computer and Mathematics Journal (PCMJ) <https://ir.uitm.edu.my/view/publication/Progress_in_Computer_and_Mathematics_Journal_=28PCMJ=29/>, 1. pp. 596-610. ISSN 3030-6728 (Submitted) https://fskmjebat.uitm.edu.my/pcmj/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Integer programming
spellingShingle Integer programming
Gastani, Anis Hafiza
Naziron, Nur Asyira
Mishan, Mohd Taufik
Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan
description 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.
format Article
author Gastani, Anis Hafiza
Naziron, Nur Asyira
Mishan, Mohd Taufik
author_facet Gastani, Anis Hafiza
Naziron, Nur Asyira
Mishan, Mohd Taufik
author_sort Gastani, Anis Hafiza
title Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan
title_short Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan
title_full Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan
title_fullStr Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan
title_full_unstemmed Dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / Anis Hafiza Gastani, Nur Asyira Naziron and Mohd Taufik Mishan
title_sort dietary supplement of collaborative recommendation system for athlete and fitness enthusiast / anis hafiza gastani, nur asyira naziron and mohd taufik mishan
publisher College of Computing, Informatics, and Mathematics
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/106073/1/106073.pdf
https://ir.uitm.edu.my/id/eprint/106073/
https://fskmjebat.uitm.edu.my/pcmj/
_version_ 1825165088925941760
score 13.239859

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