  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.
