Dynamic News Recommendation with Hierarchical Attention Network News recommendation is an effective information dissemination solution in modern society. In general, news articles can be modeled from multiple granularities: sentence, elementand newslevel. However, the first two levels have been largely ignored in existing methods and it is also unclear how such multigranularity modeling can enhance news recommendation. In this paper, we propose a novel dynamic model for news recommendation. A unique perspective of our model is to discriminate the contributions of previously interacted contents for triggering the next newsreading, in sentence, elementand newslevel simultaneously. To this end, we design a hierarchical attention network of which the lower layer learns the impacts of sentences and elements, while the upper layer captures disparity of news. Moreover, we incorporate a timedecaying factor to reflect the dynamism, as well as convolution neural networks for learning sequential influence. Using three realworld datasets, we conduct extensive experiments to verify the superiority of our model, compared with several stateoftheart approaches.

DMFP: A Dynamic Multifaceted FineGrained Preference Model for Recommendation The time signals behind a useru0027s historical behaviors are important for better inferring what she prefers to interact with at the next time. For the attentionbased recommendation methods, relative position encoding and time intervals division are two common ways to model the time signal behind each behavior. They either only consider the relative position of each behavior in the behavior sequence, or process the continuous temporal features into discrete category features for subsequent tasks, which can hardly capture the dynamic preferences of a user. In addition, although the existing recommendation methods have considered both longterm preference and shortterm preference, they ignore the fact that the longterm preference of a user may be multifaceted, and it is difficult to learn a useru0027s finegrained shortterm preference. In this paper, we propose a Dynamic Multifaceted Finegrained Preference model (DMFP), where the multihops attention mechanism and the featurelevel attention mechanism together with a vertical convolution operation are adopted to capture usersu0027 multifaceted longterm preference and finegrained shortterm preference, respectively. Therefore, DMFP can better support the next item recommendation. Extensive experiments on three realworld datasets illustrate that our model can improve the effectiveness of the recommendation compared with the stateoftheart methods.