(untitled) (International Journal of Food and Nutritional Sciences, 2022)
The increasing prevalence of dietary-related health issues underscores the need for personalized nutritional advice. This paper presents a novel approach to developing a Nutritional Recommendation Engine (NRE) using Natural Language Processing (NLP) on dietary logs. The engine leverages advanced NLP techniques to analyze and interpret user-generated dietary logs, providing personalized recommendations based on nutritional content and individual health goals. The proposed NRE utilizes a combination of text mining and sentiment analysis to extract relevant dietary information from logs. By employing Named Entity Recognition (NER) and Part-of-Speech (POS) tagging, the system identifies and categorizes key food items, nutrients, and dietary patterns. Machine learning algorithms are then used to correlate these extracted features with user-specific dietary preferences, restrictions, and health conditions. The system incorporates a knowledge base of nutritional information and dietary guidelines to enhance recommendation accuracy. It also integrates user feedback mechanisms to continuously refine its recommendations, ensuring they remain relevant and effective over time. The NRE’s design emphasizes scalability and adaptability, allowing it to cater to diverse user needs and dietary habits. Initial testing of the NRE demonstrates its effectiveness in providing actionable and personalized dietary advice, significantly improving users’ ability to make informed food choices. This paper discusses the system’s architecture, implementation details, and evaluation results, highlighting its potential to support healthier eating habits through intelligent, data-driven recommendations. The proposed Nutritional Recommendation Engine represents a significant advancement in leveraging NLP for personalized nutrition. Its innovative approach offers a promising solution to the challenges of dietary management and personalized health optimization.