To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel Recommendations

Recommendation systems are evolving predominantly across tourism, e-commerce, entertainment, and health industries. They filter information by analyzing heterogeneous network data and offering users pertinent suggestions. When choosing tourist places and hotels worldwide, many visitors and travelers frequently rely on places of interest, numerical ratings, and text reviews. This paper focuses on tourism recommendations and comprehensively reviews the field. The review highlights the types of recommendation systems, Artificial intelligence (AI), and Machine Learning (ML) in recommendation systems and the data acquisition, data processing, and feature extraction for Tourism Recommendation Systems (TRS). The development, trend analysis, theoretical foundation, and algorithmic methodologies utilized to develop recommendation systems are also covered in this work. The simulation platforms, datasets, and performance measures used to analyze TRS are reviewed and outlined. Finally, this study summarizes the status of research in the TRS and highlights the challenges, research gaps, and future directions in developing a proficient tourism recommendation system.