Recognize News Transition from Collective Behavior for News Recommendation

In the news recommendation, users are overwhelmed by thousands of news daily, which makes the users’ behavior data have high sparsity. Therefore, only considering a single user’s personalized preferences cannot support the news recommendation. How to improve the relatedness of news and users and reduce data sparsity has become a hot issue. Recent studies have attempted to use graph models to enrich the relationship between users and news, but they are still limited to modeling the historical behaviors of a single user. To fill the gap, we integrate user-news relationships and the overall user historical clicked news sequences to construct a global heterogeneous transition graph. And a refinement approach is proposed to recognize the news transition patterns in the graph. Based on the global heterogeneous transition graph, we propose a heterogeneous transition graph attention network to capture the common behavior patterns of most users to enhance the representation of user interest. Fusing the users’ personalized and common interest, we propose the GAINRec model to recommend news effectively. Extensive experiments are conducted on two public news recommendation datasets, and the results show the superiority of the proposed GAINRec model compared with the state-of-the-art news recommendation models. The implementation of our model is available at https://github.com/newsrec/GAINRec .