Research on the Algorithm of Tourism Destination Recommendation System Based on Big Data Analysis

This study proposes a hybrid recommendation algorithm combining collaborative filtering and deep learning to improve the accuracy and efficiency of the recommendation system. First, the user behavior, destination characteristics and external environment data are deeply mined through big data analysis technology to construct a high-dimensional user-destination feature matrix. Then, the collaborative filtering algorithm is used to mine user preferences, and the nonlinear feature extraction is realized by combining the deep learning model, thereby improving the cold start and data sparsity problems in traditional collaborative filtering. In addition, this study designs a hybrid recommendation strategy to comprehensively weigh the user's historical behavior and the similarity of the destination to achieve a more accurate recommendation effect. In the model simulation stage, the algorithm performance is verified using a real tourism data set. The experimental results show that the system is superior to the existing mainstream recommendation algorithms in terms of recommendation accuracy and user satisfaction, among which the recommendation accuracy is improved by 12.5 % and the user satisfaction index is improved by 8.7 %. In addition, the system has strong scalability and can adapt to data scenarios of different scales.