Research article Special Issues

Role of information security-based tourism management system in the intelligent recommendation of tourism resources

  • Received: 17 June 2021 Accepted: 02 August 2021 Published: 10 September 2021
  • With the rapid development of tourism and the Internet industry, tourism activities have increasingly become a fashion behavior of people. The role of intelligent tourism resources in tourism activities has gradually become prominent. In order to meet the needs of all kinds of users, the tourism management system services are developing in the direction of diversification and individualization, and recommending the tourism resource products that best meet the needs of users to users has become a top priority. This article aims to improve the practical value of the system through the intelligent functions of the tourism management system based on information security in the intelligent recommendation of tourism resources. The tourism management system can display the received information about tourists. Through the experimental research of the accompanying information security algorithm and the analysis of the recommendation of the tourism system, the intelligent functions of the tourism management system based on information security can be captured in the intelligent recommendation of tourism resources. Develop the tourism management system to solve efficiency problems and realize tourism management information. Experimental results show that based on information security, 80% of tourists have become a popular choice for smart recommendation countries, which will bring more convenience to tourists during the game.

    Citation: Xiang Nan, Kayo kanato. Role of information security-based tourism management system in the intelligent recommendation of tourism resources[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7955-7964. doi: 10.3934/mbe.2021394

    Related Papers:

  • With the rapid development of tourism and the Internet industry, tourism activities have increasingly become a fashion behavior of people. The role of intelligent tourism resources in tourism activities has gradually become prominent. In order to meet the needs of all kinds of users, the tourism management system services are developing in the direction of diversification and individualization, and recommending the tourism resource products that best meet the needs of users to users has become a top priority. This article aims to improve the practical value of the system through the intelligent functions of the tourism management system based on information security in the intelligent recommendation of tourism resources. The tourism management system can display the received information about tourists. Through the experimental research of the accompanying information security algorithm and the analysis of the recommendation of the tourism system, the intelligent functions of the tourism management system based on information security can be captured in the intelligent recommendation of tourism resources. Develop the tourism management system to solve efficiency problems and realize tourism management information. Experimental results show that based on information security, 80% of tourists have become a popular choice for smart recommendation countries, which will bring more convenience to tourists during the game.



    加载中


    [1] M. Li, Optimization of practical teaching system of tourism management inferior course based on learning cycle theory, J. Yuxi Normal University, 35 (2019), 112-116.
    [2] L. Xu, C. Jiang, J. Wang, J. Yuan, Y. Ren, Information security in big data: Privacy and data mining, IEEE Access, 2 (2017), 1149-1176.
    [3] X. Shao, Y. Ji, H. Le, Research and practice of cloud computing and big data in Omni-directional multi-angle information security technology, Sci. Technol. Bulletin, 33 (2017), 76-79.
    [4] Z. Trabelsi, M. A. Matrooshi, S. A. Bairaq, W. Ibrahim, M. M. Masud, Android based mobile apps for information security hands-on education, Educ. Inform. Technol., 22 (2017), 125-144.
    [5] Q. Da, J. Sun, L. Zhang, L. Kou, W. S. Wang, Q. L. Han, et al., A novel hybrid information security scheme for 2D vector map, Mobile Networks Appl., 23 (2018), 734-742. doi: 10.1007/s11036-018-0997-z
    [6] T. K. Damenu, C. Beaumont, Analysing information security in a bank using soft systems methodology, Inform. Computer Secur., 25 (2017), 240-258. doi: 10.1108/ICS-07-2016-0053
    [7] E. Kolkowska, F. Karlsson, K. Hedstrom, Towards analysing the rationale of information security non-compliance: Devising a value-based compliance analysis method, J. Strat. Inform. Systems, 26 (2017), 39-57. doi: 10.1016/j.jsis.2016.08.005
    [8] C. Xu, Y. Zhao, J. F. Zhang, H. S. Qi, System identification under information security, IFAC-PapersOnLine, 50 (2017), 3756-3761. doi: 10.1016/j.ifacol.2017.08.477
    [9] G. Xiao, Q. Cheng, C. Zhang, Detecting travel modes using rule-based classification system and gaussian process classifier, IEEE Access, 7 (2019), 116741-116752. doi: 10.1109/ACCESS.2019.2936443
    [10] J. S. Cui, M. R. Che, An intelligent recommendation system for optimization algorithms based on multi-classification support vector machine and its empirical analysis, Comp. Eng. Sci., 41 (2019), 153-160.
    [11] L. Peng, L. B. Song, D. J. Hao, Intelligent outdoor video advertisement recommendation system based on analysis of audiences' characteristics, High-Tech. News (English Edition), 22 (2016), 215-223.
    [12] A. E. Onile, R. Machlev, E. Petlenkov, Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review, Energy Rep., 7 (2021), 997-1015. doi: 10.1016/j.egyr.2021.01.090
    [13] S. Zhang, Algorithm Survey on intelligent recommendation, J. Changchun Normal University (Nat. Sci. Edit.), 36 (2017), 51-54.
    [14] Q. Li, R. Miao, J. Zhang, An intelligent recommendation method for service personalized customization, IFAC-PapersOnLine, 52 (2019), 1543-1548. doi: 10.1016/j.ifacol.2019.11.419
    [15] X. Zhai, Study on the intelligent recommendation system of personalized resources based on personas%, Sci. Technol. Inform. Develop. Econ., 3 (2018), 17-21.
    [16] S. Jaiswal, S. Virmani, V. Sethi, An intelligent recommendation system using gaze and emotion detection, Mult. Tools Appl., 78(2018), 1-20.
    [17] X. Qiao, Design of library management system based on intelligent recommendation, Microcomput. Appl., 34 (2018), 76-78.
    [18] T. C. Huang, Y. M. Huang, Where are my cooperative learning companions: Designing an intelligent recommendation mechanism, Mult. Tools Appl., 76 (2017), 11547-11565. doi: 10.1007/s11042-015-2678-2
    [19] M. Badami, F. Tafazzoli, O. Nasraoui, A case study for intelligent event recommendation, Int. J. Data Enc. Analyt., 5 (2018), 1-20. doi: 10.1007/s41060-017-0087-5
    [20] W. Yao, X. Yu, Application of intelligent recommendation system in smart community, Soft. Ind. Eng., (2016), 14-17.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2328) PDF downloads(182) Cited by(1)

Article outline

Figures and Tables

Figures(3)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog