Precision Dining: Machine Learning Techniques for Intelligent Food Recommendations

Abstract

The rise of personalized services has transformed numerous industries, and the food sector is no exception. This paper delves into the development of a machine learning-based intelligent food recommendation system designed to enhance the dining experience through precision dining. By analyzing user preferences, dietary restrictions, nutritional needs, and contextual factors such as location and time, the system generates highly personalized meal suggestions. The core of the recommendation engine employs advanced machine learning techniques, including collaborative filtering, content-based filtering, and deep learning models, to continuously learn from user feedback and refine its recommendations. We present a detailed overview of the system’s architecture, the algorithms employed, and the data processing methodologies. Additionally, we highlight the system’s performance through empirical evaluations and user studies, demonstrating significant improvements in user satisfaction and engagement. This research underscores the potential of machine learning to revolutionize food recommendations, offering tailored dining solutions that cater to individual tastes and health requirements, ultimately contributing to a more enjoyable and health-conscious dining experience.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.159-163