  The core of personalized news recommendation is accurate matching between
candidate news and user interest. Most existing news recommendation methods
usually model candidate news from its textual content and model users' interest
from their clicked news, independently. However, a news article may cover
multiple aspects and entities, and a user may have multiple interests.
Independent modeling of candidate news and user interest may lead to inferior
matching between news and users. In this paper, we propose a knowledge-aware
interactive matching framework for personalized news recommendation. Our method
can interactively model candidate news and user interest to learn user-aware
candidate news representation and candidate news-aware user interest
representation, which can facilitate the accurate matching between user
interest and candidate news. More specifically, we propose a knowledge
co-encoder to interactively learn knowledge-based news representations for both
clicked news and candidate news by capturing their relatedness in entities with
the help of knowledge graphs. In addition, we propose a text co-encoder to
interactively learn text-based news representation for clicked news and
candidate news by modeling the semantic relatedness between their texts.
Besides, we propose a user-news co-encoder to learn candidate news-aware user
interest representation and user-aware candidate news representation from the
knowledge- and text-based representations of candidate news and clicked news
for better interest matching. Through extensive experiments on two real-world
datasets, we demonstrate our method can effectively improve the performance of
news recommendation.
