Attention-Based Graph Neural Network for News Recommendation

Abstract

News recommendation aims to alleviate the big explosion of news information and helps users find their interesting news. Existing news recommendation models model users' historical click news as users' interests. Although they have achieved acceptable recommendation accuracy, they suffer from severe data sparse problems because of the limited news clicked by users. Further, the user's historical click sequence information has different effects on the user's interest, and simply combining them can not reflect this difference. Therefore, we propose an attention-based graph neural network news recommendation model. In our model, muti-channel convolutional neural network is used to generate news representations, and recurrent neural network is used to extract the news sequence information that users clicked on. Users, news, and topics are modeled as three types of nodes in a heterogeneous graph, and their relationships are modeled as edges. Graph neural network is used to effectively extract the structural information from heterogeneous graph, and helps to solve the problem of sparse data. Taking into account the different effects of different information on recommendation results, we use the attention mechanism to fuse this information distinctively. Extensive experiments conducted on the real online news datasets show that our model is superior to advanced deep learning-based recommendation methods.