An interactive food recommendation system using reinforcement learning

Food Recommendation System (FRS) assists individuals in making healthier dietary choices. However, current FRS uses collaborative filtering algorithms for one-step recommendations. Although these systems can recommend foods based on users’ historical preferences, they lack the adaptability to real-time changes in users’ health requirements and, as a result, the dynamic adjustment of recommendation strategies. This study introduces a groundbreaking approach by incorporating the dynamic and adaptive nature of reinforcement learning algorithms (RL) into FRS. The proposed multi-step recommendation framework, RecipeRL, leverages RL’s continuous decision-making and sustained interaction capabilities. To more accurately recommend foods aligned with user preferences, we introduce an effective method for expressing users’ real-time state through fused state representation. We also introduce an interactive environment to simulate authentic interactions between users and the recommendation system, enabling the system to handle multi-step recommendations. Our approach was evaluated using publicly available real-world datasets and compared to ten state-of-the-art methods. The results of the Top@10 analysis show that our method outperforms other algorithms significantly, achieving 94.68% and 95.67% for traditional Precision and the recommendation system metric NDCG, respectively. Our method also exhibits adaptability in scenarios where user preferences change, achieving 93.2% and 95.71%, respectively.