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Deep learning based personalized tourism recommendation for behavior analysis and route optimization.

Wenhua Yan¹, Xiangluo Wang², Yueqi Lu³

¹Geography and Tourism School, Luoyang Normal University, Luoyang, 471934, China. yanwenhua@lynu.edu.cn.

Scientific Reports

|November 4, 2025

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This study introduces a new deep learning framework to personalize cultural heritage tourism. It improves tourist experience and site sustainability by better predicting behavior and optimizing routes.

Area of Science:

Computer Science
Artificial Intelligence
Heritage Studies

Background:

Cultural heritage tourism faces challenges in personalization and sustainable site management.
Current recommendation systems lack the sophistication to handle complex tourist dynamics and site-specific needs.

Purpose of the Study:

To propose a novel deep learning framework for personalized cultural heritage tourism.
To enhance sustainable site management alongside improved tourist experiences.

Main Methods:

Developed Heritage-aware Graph Neural Networks (H-GNNs) for modeling cultural significance and temporal dynamics.
Introduced Cultural Spatiotemporal Transformers (C-STTs) with heritage-specific attention for balancing preservation and preferences.
Implemented Heritage-adaptive Transfer Reinforcement Learning (H-TRL) incorporating site constraints into the reward structure.

Keywords:

Cultural heritage tourism Deep learning Graph neural networks Personalized recommendation Spatiotemporal transformers Transfer reinforcement learning

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Main Results:

H-GNNs increased behavior prediction accuracy by 14%.
C-STTs reduced route optimization time by 16%.
H-TRL enhanced recommendation relevance by 12% over time.

Conclusions:

The proposed deep learning framework offers a scalable solution for personalized cultural heritage tourism.
The framework effectively balances enhanced tourist experiences with sustainable site management.
This approach demonstrates significant potential for the cultural heritage tourism industry.