Research and design of intelligent tourism personalized recommendation algorithm in big data environment

Authors

  • Yuanjing Zhu
  • Xiaolei Zhong

DOI:

https://doi.org/10.54097/jceim.v11i1.10480

Keywords:

Smart tourism, Cloud computing, A big data, Barrier-free tourism, Dijkstra algorithm

Abstract

In recent years, with the rapid development of the Internet, many information technologies of intelligent society and cash have been applied to various fields. In order to respond to the trend of technological development and use technology to improve the development of the tourism industry, the intelligence, informatization and personalization of the tourism industry have also been extensively studied. Smart tourism more refers to the application of Internet, cloud computing, Internet of Things, information processing and other technologies to achieve the integration of tourism infrastructure and tourism framework, and ultimately enable tourism departments and tourists to make wise choices. All tourists can rely on the massive data formed by big data to form a big data platform to help tourists formulate personalized tourism, digital tourism, and ultimately achieve barrier-free tourism. For tourists, the selection of attractions and the planning of amusement routes are the most troublesome. Therefore, the research focuses on the personalized recommendation algorithm for intelligent tourism services under the big data environment, and uses this algorithm to develop a tourism service robot to provide people with personalized tourism services and recommend the optimal solution to people. In this paper, the Dijkstra algorithm can be used to solve the shortest route, and the optimal route planning between multiple scenic spots is successfully realized, which verifies the scientificity and practicability of the algorithm.

References

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Published

21-07-2023

Issue

Section

Articles

How to Cite

Zhu, Y., & Zhong, X. (2023). Research and design of intelligent tourism personalized recommendation algorithm in big data environment. Journal of Computing and Electronic Information Management, 11(1), 71-73. https://doi.org/10.54097/jceim.v11i1.10480

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