Social network-based News Recommendation with Knowledge Graph

News recommendations aimed at alleviating network information overload. Traditional methods cannot simultaneously consider the representation of entities in the news at the level of knowledge and the influence of social networks on user interest points. The above two factors have a huge correlation with the efficiency of news recommendation. In order to consider these factors, this paper proposes a news recommendation model based on knowledge graph and social network which integrates knowledge graph representation and social networks into news recommendations. The model is a deep recommendation framework based on content and social networks for click-through rate prediction. The model utilizes the knowledge graph for representing entities in the news, quantifies the impact of social networks, and capture the dynamic changes of user interest. It adopts an improved sampling mechanism to quantify the social network structure. It uses a random walk sampling strategy to obtain neighbors in the social network. Moreover, it obtains the neighbor's influence weight on the target from interaction and content. The attention mechanism is used to quantify the effects of browsing records on user interests to capture dynamic changes. Experiments show that our model can effectively improve the effectiveness of news recommendations.