Research and Implementation of Fusion Machine Learning Algorithm in Tourism Recommendation

Accurate recommendation of tourist attractions is conducive to improving the travel efficiency and tourism experience of users. However, the choice of tourism feature factors and the different recommendation algorithms will affect the accuracy of scenic spot recommendation. In view of the problems of sparse data, insufficient tourism factors and low recommendation accuracy in the existing tourism recommendation research, this paper integrates the research of machine learning algorithm in tourism recommendation, which is a research project to develop the tourism recommendation fusion machine learning algorithm. The main purpose of this study is to recommend tourists according to their interests and behaviors so that they can make full use of their tourism experience when visiting new places. The main reason for developing the system is that there are many problems in the tourism industry, such as lack of tourism attraction, low service quality, high price and poor customer satisfaction. This problem can be solved by using machine learning technology and deep neural network (DNN), which has been proven to be effective in predicting future results based on past data. Therefore, we developed a DNN model for recommending tourists.