COLPOUSIT: A Hybrid Model for Tourist Place Recommendation based on Machine Learning Algorithms

Abstract

Tourism is an important sector for a country's economic growth. The travel recommendations should be made focused on better growth and attract more travelers. There is a huge amount of travel information and ideas available on the web that allows the users to make poor travel decisions. This paper focuses on building a hybrid travel recommender system by implementing collaborative-based, popularity-based, and nearby place weighted recommender system. The proposed system recommends the travel spots to the users based upon their interests and other criteria specified. In order to implement these methods, we applied a comparative study on different machine learning algorithms for collaborative-based approach and have performed weighted hybridization. These methods provide a personalized and customized list of similar places with respect to places of interest to the users. Thus, a hybrid system built using these methods provides a better recommendation of places with the advantages of these methods. The obtained results confirm that the hybrid method better than other recommender approaches when used separately.