Hybrid-based food recommender system utilizing KNN and SVD approaches

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 end-user and administrator can visualize it for better insight into decision-making.