Reinforcement Learning-Based Food Recommendation System for Dietary Optimization and Health Management
Keywords:
Food recommendation, Reinforcement learning, DDPG, Personalized diet, Nutrition management, Health monitoringAbstract
This study presents a reinforcement learning-based food recommendation model aimed at improving personalized dietary planning and long-term health outcomes. The proposed system applies the Deep Deterministic Policy Gradient (DDPG) algorithm to generate food suggestions by incorporating users’ dietary records, nutritional targets, and historical behavior patterns. Compared with conventional collaborative filtering and content-based techniques, the model adjusts recommendations over time to reflect users’ changing dietary needs. Experimental evaluations based on real-world data indicate that the system improves users’ compliance with healthy eating guidelines by 19.8% and lowers the intake of high-calorie foods by 16.3%. The results demonstrate the model’s practical value for personalized nutrition and health management.
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