A Review and Comparison on Tourism Recommender Systems

In the last decade, tourism information in the Web and the number of its users have risen. All of this information can be beneficial for users planning their trips to unknown destination. Therefore, Tourism Recommender Systems (TRSs) have attracted attention of researchers. This field has direct relevance to user interests and preferences. Selecting tourism attractions to visit a destination is one of main steps of trip plan. Although most TRSs currently focus on the first step, there are systems developed to recommend tourism attractions. In this review, we briefly explain characteristics of these TRSs in recent years (since 2008). The features we consider are: Recommendation algorithm that the designed TRS is based on, the Platform on which TRS is implemented, Context-aware, Ontology and multimedia features. Furthermore, the Functionality of TRSs are compared from the following points of views: The way of obtaining information for user modeling, Applied data mining techniques and finally factors for evaluating system.

A Review and Comparison on Tourism Recommender Systems Keywords:

context-aware systems , ontology , personalized systems , recommender systems , tourism attractions