The hybrid recommender system using deep learning for tourism in Istanbul

Istanbul, recognized for its remarkable beauty and cultural significance, is a premier destination for international travelers to Turkey. Despite its popularity, visitors frequently encounter challenges when attempting to navigate the extensive array of attractions, accommodations, and dining options available. While numerous online resources, such as blogs and forums, offer valuable information, the overwhelming volume can lead to information overload, highlighting the limitations of current recommendation technologies in the tourism sector. This research aims to develop a sophisticated recommendation system that enhances the overall tourist experience by providing personalized recommendations tailored to individual preferences and real-time location data. The increasing demand for detailed insights into local attractions including culinary experiences, shopping venues, and points of interest, necessitates a focused approach to information delivery. To address these challenges, we will utilize data sourced from the prominent platform TripAdvisor, employing a hybrid methodology that integrates collaborative filtering and content-based techniques through Artificial Neural Networks (ANNs). This approach aims to generate precise recommendations for hotels, restaurants, and attractions, ultimately improving the decision-making process for travelers. The primary objective of this study is to establish a robust recommendation system that achieves high accuracy and precision, ensuring that users receive tailored suggestions aligned with their unique needs and preferences while visiting Istanbul.