Neural News Recommendation with Collaborative News Encoding and Structural User Encoding

TL;DR

This paper introduces a novel news recommendation framework that enhances news and user representations by jointly encoding news semantics and leveraging structural user interest features, leading to improved recommendation performance.

Contribution

It proposes collaborative news encoding with cross-attention and structural user encoding using graph convolutional networks, addressing limitations of previous models.

Findings

Improved recommendation accuracy on the MIND dataset.

Effective semantic interaction modeling between news title and content.

Hierarchical user interest representation enhances personalization.

Abstract

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.