Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
Xueping Su, He Jiao, Jie Ren, Jinye Peng
Abstract
Facing the massive tourism data, the recommendation system mines the user’s interest to provide a personalized information service. The Knowledge Graph is introduced into a recommendation system, as auxiliary information can effectively solve the problems about data sparse and cold-start. Therefore, this paper proposes a new algorithm of personalized Chinese tourism recommendation based on the Knowledge Graph. First of all, because lack of the public Chinese tourism Knowledge Graph, a complete Chinese tourism Knowledge Graph is built. Secondly, a new B-TransD (Bernoulli-TransD) knowledge representation model is proposed to reduce the probability of false negative triples. Finally, the method of user interest model based on the attribute information of users and tourist attractions is proposed to improve the performance of the recommendation system. Experiments are conducted on a data set containing 9100 tourist attractions. The experimental results demonstrate that the proposed algorithm achieves significant improvement over the existing algorithms.