Visitor Navigation Pattern Prediction Using Transition Matrix Compression

International Journal of Research and Scientific Innovation (IJRSI) | Volume VII, Issue VI, June 2020 | ISSN 2321–2705

 Visitor Navigation Pattern Prediction Using Transition Matrix Compression

Ei Theint Theint Thu, Aye Aye Nyein, 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).

I. INTRODUCTION

Myanmar is one of the most interesting countries to explore in Asia.The Travels and Tours is a Myanmar-based travel agency. In other words, traveling to Myanmar is very similar to traveling back and forth to other geographical areas, not just one trip. Travel is the movement of people between distant geographical locations. It can be done by foot, bicycle, train, boat, bus, airplane and ship. Travelers may use such as bus, trains, airplanes and ships. Among them, they may travel by bus [4].

The large number of cities on many visiting places in Myanmar has raised.In Myanmar, visitors often have the first navigational question such as where can I go? The organization can advise you the popular paths in there. The system refers to tracking the visitor’s past behavior. By gathering increasing amounts of visitor’s information in organization, it can predict the next visiting places [4].

By viewing the visitor’s navigation in a Travels and Tours company, Directed Graph can construct for path forecast created on past visitors visit behavior recorded in the company’s documentation. The cities to be visited by a visitor in the forthcoming are strong-minded by visiting history in the company. Directed Graph consists of nodes representing cities, directed lines representing paths and weights on the directed lines representing the numbers of traversals on the paths.

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