Hidden markov model to predict tourists visited place
This work addresses tourism marketing needs by predicting tourist movements, but it appears incremental as it adapts an existing algorithm to a new context.
The paper tackles the problem of predicting tourists' next movements using social network data, proposing a method that adapts a grammatical inference algorithm to big data to produce a flexible hidden Markov model, with Paris used as a case study to demonstrate efficiency.
Nowadays, social networks are becoming a popular way of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated; by the propensity of tourists to share comments and photos during their trip; makes it possible to model their journeys and analyze their behavior. Predicting the next movement of tourists plays a key role in tourism marketing to understand demand and improve decision support. In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict future movements. The method relies on a machine learning grammatical inference algorithm. A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.