Smart Tourism Recommendation System Leveraging Deep Learning and Machine Learning Techniques

Abstract

Tourism significantly boosts e-commerce by guiding tourists to points of interest (POIs) among countless options online. Recommendation systems have transformed the industry by delivering accurate, preference-based suggestions, aiding in early identification and management. However, these systems often struggle with robustness and personalization when limited by insufficient personal data. In this paper presents the development of a Smart Tourism Recommendation System that leverages deep learning to deliver personalized, context-aware travel recommendations. The system employs a multi-stage approach combining content-based filtering, Neural Collaborative Filtering (NCF), and machine learning algorithms, specifically LightGBM and XGBoost, alongside LightGCN—a graph-based deep learning model for collaborative filtering