DKN: Deep Knowledge-Aware Network for News Recommendation
Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo

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…
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Taxonomy
TopicsTopic Modeling · Recommender Systems and Techniques · Advanced Graph Neural Networks
