Biometrically Anchored and Psychological States Aware Deep Learning for Enhanced Personalized Food Recommendation Systems
TL;DR
The VAE-based FRS (VAEFRS) system achieved optimization through its training process, which used a Conditional Tabular Generative Adversarial Network (CTGAN)-augmented training manifold to obtain full coverage of high-dimensional features while preventing overfitting issues.
Abstract
The current Food Recommendation System (FRS) need new methods that combine psychological and biometric data about people to develop personalized food recommendations. The existing models use static user profiles and historical interaction data, which leads to their failure to capture the dynamic context-sensitive elements that drive user behaviour. To overcome these issues, the study proposed novel FRS, uses deep learning (DL) methods to deliver personalized food suggestions which depend on user characteristics, including their gender, mood and body weight. The architectural design uses body weight as a key metabolic reference point through which it establishes physically suitable recommendations that match the behavioural patterns associated with different gender and emotional states. The proposed method includes multiple essential steps, which start with dataset collection and preparation before proceeding to create dense vector representations, which reduce dimensionality and then use Convolutional Neural Networks (CNN) to extract food-related textual features, which lead to the discovery of essential food-related text patterns. And finally, create a multi-model feature fusion which combines user preferences with biometric constraints and food attributes and then generates Top-N recommendations through a Variational AutoEncoder (VAE). The framework introduces its novel aspect through a multi-modal latent representation system, which combines temporary emotional states with metabolic needs to create health-focused recommendations that adapt in real time. The VAE-based FRS (VAEFRS) system achieved optimization through its training process, which used a Conditional Tabular Generative Adversarial Network (CTGAN)-augmented training manifold to obtain full coverage of high-dimensional features while preventing overfitting issues. Experimental results show that the proposed model achieved a hit rate@10 of 0.8929, NDCG@10 of 0.6475, precision@10 (0.3223) and recall@10 (0.3533). The results demonstrate the effectiveness of the system in delivering pertinent and accurate FRs depending on their gender, physical characteristics, and present mood.