Tourists Profiling by Interest Analysis
- URL: http://arxiv.org/abs/2512.14704v1
- Date: Fri, 05 Dec 2025 18:35:49 GMT
- Title: Tourists Profiling by Interest Analysis
- Authors: Sonia Djebali, Quentin Gabot, Guillaume Guerard,
- Abstract summary: It is now easier to examine behaviors of tourists using digital traces they leave during their travels.<n>We suggest a study focused on both qualitative and quantitative aspect of digital traces to understand the dynamics governing tourist behavior.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the recent digital revolution, analyzing of tourists' behaviors and research fields associated with it have changed profoundly. It is now easier to examine behaviors of tourists using digital traces they leave during their travels. The studies conducted on diverse aspects of tourism focus on quantitative aspects of digital traces to reach its conclusions. In this paper, we suggest a study focused on both qualitative and quantitative aspect of digital traces to understand the dynamics governing tourist behavior, especially those concerning attractions networks.
Related papers
- Hidden markov model to predict tourists visited place [2.5665716218583965]
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.
arXiv Detail & Related papers (2025-11-21T19:58:17Z) - Origin-Destination Extraction from Large-Scale Route Search Records for Tourism Trend Analysis [1.413488665073795]
The study analyzed over 380 million route search logs to investigate online search behavior related to tourist destinations.<n>The results reveal strong correlations between search volume trends and the duration of peak tourism seasons.
arXiv Detail & Related papers (2025-07-24T02:44:16Z) - Digital twins in tourism: a systematic literature review [45.498315114762484]
This systematic literature review characterizes the current state of the art on digital twinning (DT) technology in tourism-related applications.<n>Thirty-four peer-reviewed studies from three major scientific databases were selected for review.
arXiv Detail & Related papers (2025-01-03T09:26:33Z) - Multimodal video analysis for crowd anomaly detection using open access tourism cameras [76.93566452564627]
We propose the detection of crowd anomalies through the extraction of information in the form of time series from video format using a multimodal approach.
The application of this methodology on the webcam of Turisme Comunitat Valenciana in the town of Morella has provided excellent results.
arXiv Detail & Related papers (2024-05-21T11:56:01Z) - Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity.
The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies.
They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
arXiv Detail & Related papers (2024-02-14T13:20:24Z) - Forecasting Inter-Destination Tourism Flow via a Hybrid Deep Learning
Model [7.769537533798236]
ITF (Inter-Destination Tourism Flow) is commonly used for tourism management on tasks like the classification of destinations' roles and visitation pattern mining.
It is difficult to understand how the volume of ITF is influenced by features of the multi-attraction system.
We propose a graph-based hybrid deep learning model to predict the ITF.
arXiv Detail & Related papers (2023-05-05T03:48:12Z) - A Survey on Deep Learning based Time Series Analysis with Frequency Transformation [75.63783789488471]
Frequency transformation (FT) has been increasingly incorporated into deep learning models to enhance state-of-the-art accuracy and efficiency in time series analysis.<n>Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.<n>We present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT.
arXiv Detail & Related papers (2023-02-04T14:33:07Z) - Understanding Online Behaviors through a Temporal Lens [0.228438857884398]
The concept of time is under-explicated in empirical studies of online behaviors.
Time-in-behaviors perspective can provide a microscope with a renovated temporal lens to observe and understand online behaviors.
arXiv Detail & Related papers (2023-01-15T01:53:32Z) - Entities of Interest [2.609279398946235]
This dissertation revolves around discovery in digital traces, and sits at the intersection of Information Retrieval, Natural Language Processing, and applied Machine Learning.
We propose computational methods that aim to support the exploration and sense-making process of large collections of digital traces.
arXiv Detail & Related papers (2021-02-22T13:07:48Z) - Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
Country image has a profound influence on international relations and economic development.
In the worldwide outbreak of COVID-19, countries and their people display different reactions.
In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
arXiv Detail & Related papers (2020-09-12T15:54:51Z) - Learning Patterns of Tourist Movement and Photography from Geotagged
Photos at Archaeological Heritage Sites in Cuzco, Peru [73.52315464582637]
We build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit in Cuzco, Peru.
Our goals are to (1) understand how the intensification of tourism intersects with heritage regulations and social media, aiding in the articulation of travel patterns across Cuzco's heritage landscape; and to (2) assess how aesthetic preferences and visuality become entangled with the rapidly evolving expectations of tourists, whose travel narratives are curated on social media and grounded in historic site representations.
arXiv Detail & Related papers (2020-06-29T22:49:59Z) - Survey on Visual Sentiment Analysis [87.20223213370004]
This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis.
The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view.
A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
arXiv Detail & Related papers (2020-04-24T10:15:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.