Hidden markov model to predict tourists visited place

• URL: http://arxiv.org/abs/2511.19465v1
• Date: Fri, 21 Nov 2025 19:58:17 GMT
• Title: Hidden markov model to predict tourists visited place
• Authors: Theo Demessance, Chongke Bi, Sonia Djebali, Guillaume Guerard,
• Abstract summary: We propose a method to understand and to learn tourists' movements based on social network data analysis.<n>The method relies on a machine learning grammatical inference algorithm.<n>A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data.
• Score: 2.5665716218583965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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