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.

翻译：个性化新闻推荐旨在帮助用户发现与其兴趣相符的新闻文章，这在缓解用户信息过载问题方面起着关键作用。尽管近期已有许多研究致力于改进个性化新闻推荐，但以下挑战仍需进一步探索：(C1) 理解新闻文章中耦合的多种意图，(C2) 区分新闻文章阅读后产生的不同偏好，以及 (C3) 解决冷启动用户问题。为了共同应对上述挑战，本文提出了一种新颖的个性化新闻推荐框架（CROWN），该框架采用 (1) 针对 (C1) 的类别引导意图解耦，(2) 针对 (C2) 的基于一致性的新闻表征，以及 (3) 针对 (C3) 的 GNN 增强的混合用户表征。此外，我们将类别预测作为辅助任务纳入 CROWN 的训练过程，这提供了额外的监督信号以增强意图解耦。在两个真实世界数据集上进行的大量实验表明：(1) CROWN 相较于十种最先进的新闻推荐方法提供了持续的性能提升；(2) 所提出的策略显著提高了 CROWN 的准确性。