SI-News: Integrating social information for news recommendation with attention-based graph convolutional network

High-quality news recommendation heavily relies on accurate and timely representations of news documents and user interests. Social information, which usually contains the most recent information about the activities of users and their friends, naturally reflects the dynamics and diversities of user interests. However, existing news recommendation approaches often overlook these dynamic items, and thus lead to suboptimal performance. In this paper, we propose a novel approach by embedding users’ interests from their social information by attentional graph convolutional network (GCN). We also improve news representations by jointly optimizing the titles and contents of news via attention mechanisms. Extensive experiments on three benchmark datasets show that our approach effectively improves news recommendation performance compared with state-of-the-art baselines. We also evaluate our model on a real-world dataset and the results demonstrate the superior performance of the proposed techniques in industry-level applications.