JOURNEY THROUGHCENTRALASIA: DEVELOPINGASPARSEGEO-SOCIAL LOCATION-BASEDAND PREFERENCE-AWARE RECOMMENDER SYSTEMS

Abstract

The rapid growth of location-based services (LBS) has resulted in an increasing demand for personalized and context-aware recommendations. This study aims to develop a sparse geo- social location-based and preference-aware recommender system to provide accurate and relevant suggestions for users in remote areas of Central Asia. We propose a novel framework that integrates geographical, social, and preference information to address the challenges of data sparsity and user mobility in these regions. The proposed model is evaluated through extensive experiments on real- world datasets, demonstrating its
effectiveness in improving recommendation quality. Furthermore, the research highlights the potential applications of the proposed system in promoting sustainable tourism, preserving cultural heritage, and fostering social cohesion in Central Asia.