User Behavior Analysis and Prediction Algorithm in Intelligent Tourism Management System

The goal of this project is to construct an intelligent tourist management system, and to make use of big data, AI, IOT and cloud computing to realize the digital transformation of tourism industry. The system can be used in the tourism resource management, the real-time information push, the personalized recommendation, the data analysis, etc. The system includes three main modules: tourist terminal, management platform and data center, which can realize intelligent management and real-time monitoring of scenic spots, transportation, accommodation and other information. Then, this paper designs an improved algorithm combining collaborative filtering algorithm and LSTM based on the complexity and nonlinear characteristics of tourist behavior data. To prove the validity of the algorithm, we have done several simulation experiments. The data are obtained from the real user interaction data set of an intelligent tourist platform. The experiment results indicate that the proposed algorithm has better performance than the conventional LSTM. The recommendation precision is 12.5% and the recall rate is 10.8%. In addition, the system model also shows good performance in computational efficiency and prediction robustness, and is suitable for large-scale tourism data scenarios.