  News articles usually contain knowledge entities such as celebrities or
organizations. Important entities in articles carry key messages and help to
understand the content in a more direct way. An industrial news recommender
system contains various key applications, such as personalized recommendation,
item-to-item recommendation, news category classification, news popularity
prediction and local news detection. We find that incorporating knowledge
entities for better document understanding benefits these applications
consistently. However, existing document understanding models either represent
news articles without considering knowledge entities (e.g., BERT) or rely on a
specific type of text encoding model (e.g., DKN) so that the generalization
ability and efficiency is compromised. In this paper, we propose KRED, which is
a fast and effective model to enhance arbitrary document representation with a
knowledge graph. KRED first enriches entities' embeddings by attentively
aggregating information from their neighborhood in the knowledge graph. Then a
context embedding layer is applied to annotate the dynamic context of different
entities such as frequency, category and position. Finally, an information
distillation layer aggregates the entity embeddings under the guidance of the
original document representation and transforms the document vector into a new
one. We advocate to optimize the model with a multi-task framework, so that
different news recommendation applications can be united and useful information
can be shared across different tasks. Experiments on a real-world Microsoft
News dataset demonstrate that KRED greatly benefits a variety of news
recommendation applications.
