A Personalized Recommendation Method for Hotel Room Management Based on Behavioral Preference Model

To address the issue of low precision in personalized recommendations for hotel room management, a personalized recommendation method for hotel room management based on a behavioral preference model is proposed. High-quality customer behavior data were collected. The Latent Dirichlet Allocation model was utilized to cluster keywords and conduct sentiment tendency analysis. The customer preference characteristics were quantitatively calculated, and a behavioral preference model was constructed. Then, personalized room recommendations were achieved by combining with the collaborative filtering recommendation algorithm. The experimental results show that this method can reduce the maximum value of the Mean Absolute Error (MAE) of recommendations to 0.12, increase the F1 value to a maximum of 0.80%, and the overall comprehensive recommendation efficiency is higher than 90%. The innovation of this research lies in accurately capturing customers’ dynamic preferences through the behavioral preference model. Compared with the comparative methods, it provides a more accurate and effective personalized room management service.