The simulation method used to construct the MealRec+ dataset can be further improved or extended by incorporating more dynamic and personalized user behaviors. One way to achieve this is by implementing reinforcement learning techniques to simulate user decision-making processes based on feedback and rewards. By modeling user preferences and behaviors in a more adaptive and responsive manner, the simulation can better capture the nuances of real-world dining experiences. Additionally, integrating natural language processing (NLP) models to analyze user reviews, comments, and feedback can provide valuable insights into user preferences and dining habits, leading to more realistic simulations.