  Personalized news recommendation aims to assist users in finding news
articles that align with their interests, which plays a pivotal role in
mitigating users' information overload problem. Although many recent works have
been studied for better personalized news recommendation, the following
challenges should be explored more: (C1) Comprehending manifold intents coupled
within a news article, (C2) Differentiating varying post-read preferences of
news articles, and (C3) Addressing the cold-start user problem. To tackle the
aforementioned challenges together, in this paper, we propose a novel
personalized news recommendation framework (CROWN) that employs (1)
category-guided intent disentanglement for (C1), (2) consistency-based news
representation for (C2), and (3) GNN-enhanced hybrid user representation for
(C3). Furthermore, we incorporate a category prediction into the training
process of CROWN as an auxiliary task, which provides supplementary supervisory
signals to enhance intent disentanglement. Extensive experiments on two
real-world datasets reveal that (1) CROWN provides consistent performance
improvements over ten state-of-the-art news recommendation methods and (2) the
proposed strategies significantly improve the accuracy of CROWN.
