Citation

Yap, Zhi Toung and Haw, Su Cheng and Ruslan, Nur Erlida (2024) Hybrid-based food recommender system utilizing KNN and SVD approaches. Cogent Engineering, 11 (1). ISSN 2331-1916

Text Hybrid-based food recommender system utilizing KNN and SVD approaches.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

In the era of digital platforms and abundant data, food recommender systems have been essential tools for guiding individuals to discover preferences and perfect meals. Nowadays, the wide variety of available food options presents a challenge for consumers seeking personalized meals and relevant recommendations. By dynamically allocating evaluations based on user behaviour and item characteristics, the system aims to increase the variety and precision of dietary recommendations. Furthermore, the system will implement continuous learning mechanisms to responds to fluctuations in user preferences over time, ensuring sustained high levels of user satisfaction. Therefore, the primary objective of this paper is to design and implement the recommender system, test and evaluate the hybrid recommender system and explore the various recommendation techniques. Besides that, this paper will discuss the combination of various algorithms: collaborative filtering, content-based filtering, and hybrid approaches. The expected outcome of this research is a robust recommender system that provides accurate and relevant food recommendations to individual preferences. In conclusion, a system with a graphical user interface will be implemented so that the enduser and administrator can visualize it for better insight into decision-making.

Item Type:	Article
Uncontrolled Keywords:	Food recommendation system, Algorithms & Complexity; Artificial Intelligence; Computer Science (General
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2025 05:42
Last Modified:	03 Jan 2025 05:42
URII:	http://shdl.mmu.edu.my/id/eprint/13299

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