Graph neural news recommendation based on multi-view representation learning

Accurate news representation is of crucial importance in personalized news recommendation. Most of existing news recommendation model lack comprehensiveness because they do not consider the higher-order structure between user-news interactions, relevance between user clicks on news. In this paper, we propose graph neural news recommendation based on multi-view representation learning which encodes high-order connections into the representation of news through information propagation along the graph. For news representations, we learn click news and candidate news content information embedding from various news attributes. And then combine obtained structure-based representations with representations from news content. Besides, we adopt a candidate-aware attention network to weight clicked news based on their relevance with candidate news to learn candidate-aware user interest representation for better matching with candidate news. The performance of the model has been improved in common evaluation metric. Extensive experiments on benchmark datasets show that our approach can effectively improve performance in news recommendation.