Tourists Profiling by Interest Analysis

2025-12-05

International Conference on Advanced Data Mining and Applications

AI Summary

Traditional statistical methods struggle to uncover the underlying motivations behind tourist behavior and interest evolution within attraction networks. Method: We propose the first integrated analytical framework that jointly models semantic features (via LDA topic modeling of textual digital footprints) and structural features (via visit-sequence graph modeling), enhanced by graph neural networks (GNNs) and multi-source trajectory clustering. This enables interpretable tourist interest profiling and cross-city mobility mapping. Contribution/Results: Evaluated on real-world tourism datasets, our approach achieves a 23.6% improvement in interest cluster identification accuracy. It significantly advances understanding of how tourist preferences form and shift over time and space. By bridging semantic intent with spatiotemporal behavioral structure, the framework establishes a novel paradigm for intelligent tourism recommendation and collaborative destination management.

Technology Category

Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityNatural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument MiningPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsWeb Mining and Content Analysis: Bridging structured and unstructured dataSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Analyzing tourist behaviors using digital traces
• Combining qualitative and quantitative aspects of data
• Understanding dynamics of tourist attraction networks

Innovation

• Combines qualitative and quantitative digital trace analysis
• Analyzes tourist behavior through attraction networks dynamics
• Profiles tourists by interest using digital footprints