基于知识图谱用户偏好传播的实体推荐模型.

Knowledge graph is a useful tool when introducing it into the recommendation system as auxiliary information. It can effectively enhance the learning ability of the recommendation system, improving the system' s accuracy and user' s satisfaction. Aiming at the problem of the preference propagation on the knowledge graph, this paper proposed an entity recommendation model based on the user preference propagation of the knowledge graph. This model took the transmission intensity into consideration, while propagated the preference at the same time, thus improved the final effect of recommendation. It controlled the propagation intensity of user's preference on the knowledge graph by extracting the basic characteristics of different specific attributes, and iteratively calculated the historical preference data of each user to obtain the preference propagation model of user-item pair. Later, employing the sorting learning algorithm to get the top N recommendations. In the end, comparison experiments on three different kinds of datasets verified the effectiveness of the proposed model. This study shows that controlling the propagation intensity during the propagating process can significantly improve the accuracy rate, recall rate,as well as the F, value of the recommendation system, and this method also has strong flexibility and interpretability. [ABSTRACT FROM AUTHOR]