Recommender System based on Learning Techniques for Tourists

Abstract: Choosing a tourist destination from the knowledge that's available on the web and thru other sources is one among the foremost complex tasks for tourists when planning travel, both before and during travel. Previous Travel Recommendation Systems have attempted to solve this problem. This paper proposes a novel Travel Recommendation System that recommends destinations to tourists that are mostly visited based on the tourists dataset taken. It considers both technical and practical aspects using a real world data set we collected. The system is developed using a two-steps feature selection method to reduce number of inputs to the system and recommendations are provided by decision tree C4.5. The experimental results show that the proposed Travel Recommendation System can provide recommendation on tourist destinations that are mostly visited.

Keywords: Travel Recommendation System, Destination, c4.5Decision tree, Feature selection, Tourists.