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 user and news representations, the following challenges have been rarely studied: (C1) How to precisely comprehend a range of intents coupled within a news article? and (C2) How to differentiate news articles with varying post-read preferences in users' click history? To tackle both challenges together, in this paper, we propose a novel personalized news recommendation framework (CIDER) that employs (1) category-guided intent disentanglement for (C1) and (2) consistency-based news representation for (C2). Furthermore, we incorporate a category prediction into the training process of CIDER as an auxiliary task, which provides supplementary supervisory signals to enhance intent disentanglement. Extensive experiments on two real-world datasets reveal that (1) CIDER provides consistent performance improvements over seven state-of-the-art news recommendation methods and (2) the proposed strategies significantly improve the model accuracy of CIDER.

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