Tourism Recommendation System based on Knowledge Graph Feature Learning

Tourism recommendation system based on the knowledge graph feature learning is proposed and designed in this paper. The primary task for implementing a travel recommendation system is data collection, including user information, integrated user interaction records, tourist attraction information, and also contextual information. Among them, the user information primarily originates from the information entered by user in the registration process. The interaction record between the user and the system can be obtained from the system log, while the contextual information is entered by the user autonomously or obtained through various sensors. In this paper, a data processing and analytic framework is integrated to construct the novel scenario used for the recommendation. When compared the proposed model with the state-of-the-art research works, it has been proven that the proposed model can obtain the higher recommendation accuracy.