Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Abstract

Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recommendation systems (TRS) provide several benefits, including personalization, convenience, improved user experience, and increased revenue for tourism businesses. These systems can suggest destinations, attractions, accommodations, and activities that match user preferences and past behaviors, ultimately simplifying the trip planning process. Machine learning algorithms can be trained on large datasets to generate personalized recommendations, and can continuously improve their effectiveness by incorporating new data and user feedback. This paper provides a state-of-the-art overview of various types of recommendation systems (RS), including those based on user preferences, behaviors, demographic profiles, and social network judgments. The paper also presents a comparison table for these approaches. Additionally, the paper discusses the different stages of the travel process and the sources of data that can be used to develop a recommender system. The concluding section of the paper highlights the importance of personalized recommendations in the tourism industry and the potential for future research in this area.