The Tourism Recommendation Based On Ontology

With the development of the Internet, people usually get information from the network.Currently it has become the most popular way to get travel information from network. With the e-commerce sites on the Internet become more and more popular. Tourists often get lost in lots of tourism information and tourism products.In this paper, the existing tourism recommendation system has been expanded. Firstly using association rules dig out related users from a large number of users. By this way users in the database had been divided into two parts which are related users and no related users.Secondly the related users adopt the algorithm which is collaborative filtering algorithm added the time factor and the evaluation factor to recommend. And the no related users adopt the collaborative filtering algorithm added the time factor and the attractions ontology to recommend. Then mix these two parts results. Using association rules can reduce computational complexity and improve the speed of operation. Considering the no related users can make sure the integrity of data. Adding time factor, evolution factor and attractions ontology to traditional collaborative filtering algorithm can improve efficiency and precision.Thirdly filter useless information according to the context information. Finally extended attractions into tourism information based on tourism ontology.The experimental results show that the tourism recommendation algorithm has good performance in every evaluation index. The new method avoids the incomplete and useless information appears in tourism recommendation.