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Related Experiment Video

Updated: Jun 5, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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Enabling personalized smart tourism with location-based social networks.

Yuqi Shen¹, Yuhan Wu², Jingbo Song³

¹College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.

Peerj. Computer Science

|December 9, 2024

Summary

This study introduces a knowledge-driven deep learning method for personalized smart tourism recommendations using location-based social networks (LBSNs). It addresses data challenges to enhance travel planning and understand mobility patterns.

Keywords:

Artificial intelligence Location-based social networks Personalized recommendation system Smart tourism

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Area of Science:

Computer Science
Information Science
Tourism Studies

Background:

The tourism industry is rapidly evolving due to advancements in mobile internet, IoT, and communication technologies, leading to the rise of smart tourism.
Location-based social networks (LBSNs) offer rich data for smart tourism but face challenges in multi-source information modeling and data sparsity.
Personalized travel planning and recommendations are key features of smart tourism, requiring effective data analysis techniques.

Purpose of the Study:

To propose a novel knowledge-driven personalized recommendation method for smart tourism by leveraging deep learning on LBSN data.
To address the complexities of multi-source information modeling and data sparsity inherent in LBSN data for tourism applications.
To enhance user travel planning and recommendations by exploring personalized travel behaviors and understanding human mobility patterns.

Main Methods:

Utilized representation learning techniques to model contextual information (time, space, semantics) within LBSNs.
Employed contrastive learning techniques for data augmentation to explore user behaviors and mitigate data sparsity.
Conducted a case study focused on trip recommendation to validate the proposed approach.

Main Results:

The proposed knowledge-driven method effectively models contextual information from LBSNs for personalized recommendations.
Contrastive learning strategies successfully alleviated data sparsity and revealed user personalized travel behaviors.
The case study demonstrated the practical effectiveness of the approach in trip recommendation.

Conclusions:

The developed deep learning method offers a robust solution for personalized smart tourism recommendations using LBSN data.
The approach effectively handles complex data challenges, paving the way for more intelligent tourism services.
Insights into human mobility patterns were gained by analyzing contextual data and tourist preferences.