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.

翻译：个性化新闻推荐旨在帮助用户发现符合其兴趣的新闻文章，在缓解用户信息过载问题中起着关键作用。尽管近期已有许多研究致力于优化用户与新闻的表示，但以下挑战尚未得到充分关注：（C1）如何精准理解新闻文章中蕴含的多种意图？（C2）如何区分用户点击历史中具有不同阅读后偏好的新闻文章？为同时应对这两项挑战，本文提出了一种新颖的个性化新闻推荐框架（CIDER），该框架采用（1）类别引导的意图解耦机制以解决（C1），以及（2）基于一致性的新闻表示方法以解决（C2）。此外，我们将类别预测作为辅助任务融入CIDER的训练过程，通过提供补充监督信号来增强意图解耦。在两个真实数据集上的大量实验表明：（1）CIDER在七种最先进的新闻推荐方法上均取得持续性能提升；（2）所提策略显著提高了CIDER的模型准确率。