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Visitor Navigation Pattern Prediction Using Transition Matrix Compression

Author

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  • Ei Theint Theint Thu

    (Faculty of Information Science,University of Computer Studies, Hinthada, Myanmar)

  • Aye Aye Nyein

    (Faculty of Information Science,University of Computer Studies, Hinthada, Myanmar)

  • Hlaing Htake Khaung Tin

    (Faculty of Information Science,University of Computer Studies, Hinthada, Myanmar)

Abstract

As an increasing number of cities consists of an increasing number of visiting places, it is more difficult for the visitors to consider. Meanwhile, the system tries to introduce recommendation features to their visitors. The main aim of this paper is to only implement visitor navigation pattern prediction system in Myanmar. The paper uses traveling paths that assist visitors to navigate the visiting places based on the past visitor’s behavior. To cluster the paths with similar transition behavior and compress the transition matrix to an best size for efficient probability calculation in paths, transition probability matrix compression has been used. In this paper, Visitor Navigation Pattern Prediction Using Transition Matrix Compression is developed. It uses data mining techniques for recommending a visitor which (next)paths is closely the most popular paths in Myanmar. By looking at the traveling paths in the organization, the system can know the popular paths(places).

Suggested Citation

  • Ei Theint Theint Thu & Aye Aye Nyein & Hlaing Htake Khaung Tin, 2020. "Visitor Navigation Pattern Prediction Using Transition Matrix Compression," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 7(6), pages 113-116, June.
    • Handle: RePEc:bjc:journl:v:7:y:2020:i:6:p:113-116
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