Personalized Food Recommendation System Using Nearest Neighbors for Nutritional and Preference-Based Filtering

DOI

10.2991/978-94-6463-718-2_142How to use a DOI?

Keywords

personalized food recommendation; nearest neighbors; nutritional filtering; dietary preferences; machine learning; real-time adaptability; scalability; inclusivity; user satisfaction; health-focused applications

Abstract

The personalized food recommendation systems are crucial for enhancing the user satisfaction by catering their personal diet choice and nutrition needs. The new food recommendation models are discussed in this research using the Nearest Neighbors algorithm to improve accuracy and inclusiveness of meals. Existing systems tend to be focused narrowly on prefactoring user preferences, or disease states, failing to leverage the full breadth of nutritional science, active dynamic user feedback, and over quasit information to derive balanced adaptive dietary recommendations. The model is trained on real world datasets to address various populations and dietary requirements, surpassing other limitations such as scalability and computational efficiency. This research fulfills those aspects of gap that previous studies are not able to cover like focused health, specific technology and limited users to implement and validate and provides a user-centred personalized food recommendation solution that is robust, easy to use and validate. Compared to the existing systems, the proposed system excels in real-time adaptability, inclusiveness, and usability over a prolonged period, thus paving the way for future advancements.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Sutharsan
AU  - S. Vigneshwaran
AU  - P. Vimalraj
AU  - E. Baby Anitha
AU  - R. Vijhayalakshme
AU  - K. Nithya
PY  - 2025
DA  - 2025/05/23
TI  - Personalized Food Recommendation System Using Nearest Neighbors for Nutritional and Preference-Based Filtering
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1709
EP  - 1718
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_142
DO  - 10.2991/978-94-6463-718-2_142
ID  - Sutharsan2025
ER  -