DKN: Deep Knowledge-Aware Network for News Recommendation

TL;DR

This paper introduces DKN, a deep learning model that incorporates knowledge graphs into news recommendation to improve personalization by capturing semantic and knowledge-level connections, addressing news timeliness and user interest diversity.

Contribution

The paper presents a novel multi-channel CNN architecture that aligns words and entities, integrating external knowledge into deep news recommendation models.

Findings

DKN outperforms state-of-the-art models in click-through rate prediction.

Knowledge integration significantly improves recommendation accuracy.

The attention module effectively captures user interest diversity.

Abstract

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.