Work in Progress
With the upgrade of tourism informationization and the rise of intelligent city construction, the Internet of Things, Cloud Computing and other information technologies have been widely used in the tourism industry, making the development model and technical framework of intelligent tourism become a hot issue. Aiming at the problems of insufficient preparation and inadequate reception capacity of scenic spots in recent years, this paper proposes to apply machine learning algorithm to predict the number of tourists, so as to make an early response. In this paper, the characteristics of the application of the number of tourists are analyzed. The fixed buffer kernel online gradient descent algorithm is used to predict the number of tourists, and the actual number of tourists is brought into the algorithm for experiments. Finally, the rationality of the experimental process and results is analyzed.
Ren, Qinghui; Li, Shenglin; Song, Bo; and Chen, Chen, "The application of Bounded Online Gradient Descent Algorithms for Kernel Based Online Learning in Tourist Number Forecasting" (2018). ICEB 2018 Proceedings. 12.