News recommendation models often fall short in capturing users' preferences due to their static approach to user-news interactions. To address this limitation, we present a novel dynamic news recommender model that seamlessly integrates continuous time information to a hierarchical attention network that effectively represents news information at the sentence, element, and sequence levels. Moreover, we introduce a dynamic negative sampling method to optimize users' implicit feedback. To validate our model's effectiveness, we conduct extensive experiments on three real-world datasets. The results demonstrate the effectiveness of our proposed approach.

翻译：新闻推荐模型往往因采用静态方法处理用户与新闻的交互而难以准确捕捉用户偏好。为解决这一局限，我们提出了一种新型动态新闻推荐模型，该模型将连续时间信息无缝集成至层次注意力网络中，从而在句子级、元素级和序列级有效表征新闻信息。此外，我们引入了一种动态负采样方法以优化用户的隐式反馈。为验证模型有效性，我们在三个真实世界数据集上开展了广泛实验，结果证明了所提方法的优越性。