Abstract

Project recommendation plays an important role in tourism management. It can not only promote the overall development of tourism, but also improve the satisfaction of tourists. However, in the process of tourism recommendation, there are some problems, such as insufficient personalized recommendation, low satisfaction with recommendation, inaccurate recommendation and so on. Therefore, this paper proposes a personalized tourism recommendation algorithm based on remote network control technology, which classifies the tourism needs of different groups and summarizes the data. On this basis, the tourism resources database is constructed, and the tourism resources are numbered. Combined with map list, real-time location of tourists, weather conditions, traffic conditions and other information, it provides tourists with more accurate and practical tourism recommendation services. Finally, through remote network control technology, the matching of tourism resources and demand can be realized, and the accurate transmission of personalized recommendation data can be ensured, and the recommendation algorithm can be promoted. The research results show that under the remote network control technology, personalized recommendation algorithm can improve the matching rate of all tourism resources, realize the rational development of tourism resources, and meet various needs of tourists.

Abstract

Fog computing provides reliable computing, customized and by guarantee dynamic environments for the end-users, so that more and more people tend to use fog environments every day in their workplace. Virtualization is an integral part of fog. The purpose of using fog computing systems is to minimize the cost of using a virtual machine by service providers and also to maximize revenue from providing service to consumers' applications. Scheduling is a process that considers several parameters simultaneously to optimize the execution of requests in fog computing networks, such as the queue duration of requests and the speed of virtual machine, and the priority of requests, and these parameters result in the success or failure of a request on virtual machine. In this study, we provided a new method of scheduling using ant colony algorithm, which can perform scheduling and execution of tasks with higher quality.