User Behavior Analysis-based Personalized Recommendation and Prediction Model in Guidance Systems Algorithm Research

With the acceleration of urbanization, the application of guidance systems in public environments has become increasingly important. This paper focuses on the analysis of user behavior in guidance systems and proposes a predictive model algorithm based on personalized recommendation (DUCL). The algorithm integrates user behavior characteristics to deeply explore users’ navigation needs. Unlike traditional personalized recommendations, our algorithm not only analyzes user behavior patterns in real-time but also dynamically adjusts recommendation content based on updated environmental data. Specifically, the algorithm first constructs a multi-dimensional behavior feature model, which comprehensively considers users’ real-time behavior, historical behavior, and contextual information, and models user behavior data in high-dimensional features to more accurately capture changes in users’ interests. Next, we designed a dynamic interest update mechanism based on a time-decay function, which can capture the trend of changes in user interests in real-time and enhance the timeliness of recommendations. Finally, we integrated collaborative filtering algorithms with deep learning methods, using convolutional neural networks (CNN) and long short-term memory networks (LSTM) to achieve in-depth mining of user behavior, thereby improving the accuracy and personalization of recommendation results. In addition, we proposed a hybrid recommendation strategy combining content-based recommendation and user behavior-based recommendation to ensure high recommendation quality even when data is sparse. The algorithm accurately captures user preferences, enhancing the relevance and satisfaction of recommendations. The experimental results indicate that the proposed model significantly outperforms traditional guidance systems in terms of recommendation accuracy and user experience, effectively improving user navigation efficiency and reducing the time cost of information acquisition. In summary, this paper not only provides a new perspective for the application of guidance systems but also lays the foundation for related research on intelligent urban development.