Enhancing Point-of-Interest Recommendation Systems through Multi-Modal Data Integration in Location-Based Social Networks: Challenges and Future Directions

Abstract

The rapid development of location-based networks (LBSNs) has transformed many aspects of tourism and management by changing the way travelers interact with destinations. However, changes in data analysis pose a major challenge to POI computational models, limiting their ability to generate accurate, reliable language and information. This study reviews recent advances in integrating multiple datasets, machine learning algorithms, and artificial intelligence to improve POI recommendations. In particular, it explores integration techniques, opinion analysis, and context-awareness techniques to mitigate data constraints. Furthermore, the study highlights the importance of real-time monitoring and creative strategies in sustainable management, especially in improving user experience and trust. The transparency and reliability of AI-based recommendations are essential for user adoption, optimization, and informed decision-making. This study also demonstrates the integration of spatial, temporal, and spatial information to improve the accuracy of recommendations and address issues such as bias, variability, and change over time. Using artificial intelligence (AI) and machine learning (ML), this paper presents future directions for generating accurate, user-friendly, and adaptive POI recommendations. Addressing data inconsistencies and enabling informed decision-making can lead to robust and sustainable LBSN-based systems, ultimately transforming the tourism experience and services of the region.