A Personalized Recommendation Algorithm for Tourist Attractions Using User Behavior Data

Xiaolei Zhong; Ze Chen; Rui Qiao; Hongwei Ding; Rong Zong

doi:10.62051/ijcsit.v3n2.28

Authors

Xiaolei Zhong
Ze Chen
Rui Qiao
Hongwei Ding
Rong Zong

DOI:

https://doi.org/10.62051/ijcsit.v3n2.28

Keywords:

Smart tourism, Cloud computing, Edge computing, Android application, Personalized recommendation

Abstract

With the widespread application of 5G and cloud computing technologies, smart tourism has become a hotspot and development trend in the tourism industry. This paper develops a cloud-based intelligent tourism application for the Android platform, innovatively integrating edge computing technology to achieve faster data processing speeds and lower latency, providing users with instant travel information and services. The application adopts a personalized recommendation algorithm to intelligently recommend tourist attractions and routes based on user behavior and preferences, and introduces a geo-tagged social function, allowing users to share travel experiences and reviews, enhancing user interaction and community sense. Furthermore, the application incorporates augmented reality (AR) technology, providing virtual tour guide services through the mobile camera, bringing users an immersive travel guide experience. This paper details the design architecture, key technology implementation, and expected effects of this intelligent tourism application, aiming to enhance the tourism experience while promoting the digital transformation and development of the tourism industry through technological innovation.

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