User Profiling in Travel Recommender System using Hybridization and Collaborative Method

The recommender system is improving with the increase in the information obtained from numerous application domains. Recommendation or the prediction of an item depends on the rating and review given by an individual or a group of customers. New user information can also be predicted using the searching history or the profile information of the customer. In Travel Recommender System, the locations of interest are figured out based on the activities carried out by the user or the preference of that particular user. It also helps in exploring the diverse geographical areas of interest of the user. The increasing demands of this system enhances the scope in development of user behaviour that is based on recommendation approaches. It also effectively deals with the sparsity problem by searching through a large amount of data to provide users with individual contents and services. This article explores the various aspects and potentiality of existing approaches in the Travel Recommender System based on user profiles for future research directions and recommendation framework.