A Systematic Survey of Tourism Recommender System Techniques and Challenges

Abstract

Since the epidemic, there has been a significant drop in the tourism sector, thereby affecting several tourism-dependent nations. However, after COVID-19, people have been wanting to go out to visit places. Since the online era has been trending, people might take help from some websites that use recommender systems to suggest the desired destination which includes basic amenities like the places to visit, hotels, traveling, food, etc. to the user. This paper focuses on some key ideologies that are mostly used in most of the user searches. It discusses the latest machine learning and deep learning techniques used in tourism recommendation and the challenges that are being faced to date by the recommender systems. Recommender systems help to organize a large amount of data available online just by getting the reviews and looking at the previous history of the users. The latest papers have been classified and the papers are analyzed and categorized according to their applications.