Adaptive Meal Optimizer with Behavioral Based Personalization
Saumya Dwivedi, Maazin Hayath Babu, R. N. Ashlin Deepa, G. Niranjana, Rajendrane Rajmohan · 2025
In this paper, we present Adaptive Meal Optimizer with Behavioral Based Personalization, a recommendation system that recommends personalized meal suggestions by integrating content based and collaborative filtering with a LightGBM based ranking model. It is the system for when contextual recommendations are required with a wide variety of possible choices based on user behavior, dietary preferences and nutritional goals. Semantic tag matching utilizes the Natural Language Processing (NLP) techniques and the diversity logic is used to reduce repetition. By using the proposed architecture, the accuracy and user satisfaction needs can be greatly improved, and that provides a scalable solution to intelligent dietary planning.