An enhanced tourism recommendation system with relevancy feedback mechanism and ontological specifications

Abstract

Data mining is an analytic process used to access data in search of consistent patterns from the database and it is used to get relevant results. This paper describes a recommender system that helps travel agents in recommending tourism options to the customers, especially those who do not know where to go and what to do. This process describes textual messages exchanged between a travel agent and a user through a chat box. Text mining technique analyzes an interesting area in the messages. Then, the system seeks a database and accesses tourist options like attractions and cities. The system provides travel package recommendations to the customers for their choice. Travel agent created travel packages using test Approach. Here, the classification of text queries is done using Rocchio classification algorithm. The final results yielded using ontological specifications (OS) are compared with that without ontological specifications (WOS). OS-based systems’ relevancy is comparatively good.