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

A Personalized Recommendation Algorithm for Tourist Attractions Using User Behavior Data

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

International Journal of Computer Science and Information Technology, (2024), Vol.3, No.2, pp.242-254

Published: July 19, 2024

DOI: 10.62051/ijcsit.v3n2.28

PDF: Download Full Text PDF

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.

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

APA Citation: Xiaolei Zhong, Ze Chen, Rui Qiao, Hongwei Ding, Rong Zong (2024). A Personalized Recommendation Algorithm for Tourist Attractions Using User Behavior Data. International Journal of Computer Science and Information Technology, 3(2), 242-254. https://doi.org/10.62051/ijcsit.v3n2.28

References

Sun, Z., Luo, Y., Zheng, X., & Wang, J. (2021). A fusion method of user sentiment and similarity for intelligent tourism path recommendation. Computer Science, 48(S1), 226-230. https://doi.org/10.1016/j.jksus.2021.03.024
Gong, N. (2024). Floyd multi-source shortest path algorithm parallelization research. Modern Computer, 30(01), 66-69. https://doi.org/10.1016/j.moco.2023.12.008
Han, X. (2023). Multi-path planning based on constraint clustering and particle swarm algorithm. Computer and Modernization, (08), 7-11. https://doi.org/10.1016/j.cm.2023.06.002
Hou, Y., & Ma, Z. (2023). Hierarchical network shortest path algorithm based on depth-first search. Wireless Communication Technology, 49(04), 684-688. https://doi.org/10.1016/j.wct.2023.03.019
Zhao, Y., & Zhou, X. (2023). Research on the shortest path of road network based on PIN-LRU algorithm. Surveying and Mapping Technology Equipment, 25(02), 11-16. https://doi.org/10.1016/j.smte.2023.02.001
Chen, G., Lian, Y., & Li, X. (2023). Link prediction method based on three-stage path adaptive degree penalty. Sichuan University of Science & Engineering Journal (Natural Science Edition), 36(03), 59-67. https://doi.org/10.1016/j.susej.2023.01.004
Wang, J. (2023). Research on dynamic calculation method of the shortest time path based on ant colony algorithm. Wireless Interconnection Technology, 20(10), 141-143. https://doi.org/10.1016/j.wit.2023.08.006
Li, P. (2018). Application of traffic index around scenic spots and regional traffic saturation index for intelligent transportation travel decision. Intelligent City, 4(11), 1-3. https://doi.org/10.1016/j.intc.2018.10.001
Yue S, Lirong S. Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm [J]. Wireless Communications and Mobile Computing, 2022, 2022.
Peng S. Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment [J]. International Journal of Information Technologies and Systems Approach (IJITSA), 2023, 16(3):1-13.
Wang F. IoT for smart English education: AI-based personalised learning resource recommendation algorithm [J]. International Journal of Computer Applications in Technology, 2023, 71(3):200-207.
Changfei T, Jun Z. An intelligent deep learning-enabled recommendation algorithm for teaching music students [J]. Soft Computing, 2022, 26(20):10591-10598.
Uddin, Mohi M K, Dey, et al. MirrorME: implementation of an IoT based smart mirror through facial recognition and personalized information recommendation algorithm [J]. International Journal of Information Technology, 2021, 13(6):1-10.
Li, Bing, Li, et al. Hybrid recommendation algorithm of cross-border e-commerce items based on artificial intelligence and multiview collaborative fusion [J]. Neural Computing and Applications, 2021, 34(9):1-10.
Yuxuan G, Haiming L, Bingzhen S. Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform [J]. Journal of Intelligent & Fuzzy Systems, 2021, 40(5):9173-9185.