  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.
