  Automatic news recommendation has gained much attention from the academic
community and industry. Recent studies reveal that the key to this task lies
within the effective representation learning of both news and users. Existing
works typically encode news title and content separately while neglecting their
semantic interaction, which is inadequate for news text comprehension. Besides,
previous models encode user browsing history without leveraging the structural
correlation of user browsed news to reflect user interests explicitly. In this
work, we propose a news recommendation framework consisting of collaborative
news encoding (CNE) and structural user encoding (SUE) to enhance news and user
representation learning. CNE equipped with bidirectional LSTMs encodes news
title and content collaboratively with cross-selection and cross-attention
modules to learn semantic-interactive news representations. SUE utilizes graph
convolutional networks to extract cluster-structural features of user history,
followed by intra-cluster and inter-cluster attention modules to learn
hierarchical user interest representations. Experiment results on the MIND
dataset validate the effectiveness of our model to improve the performance of
news recommendation. Our code is released at
https://github.com/Veason-silverbullet/NNR.
