  The news recommender systems are marked by a few unique challenges specific
to the news domain. These challenges emerge from rapidly evolving readers'
interests over dynamically generated news items that continuously change over
time. News reading is also driven by a blend of a reader's long-term and
short-term interests. In addition, diversity is required in a news recommender
system, not only to keep the reader engaged in the reading process but to get
them exposed to different views and opinions. In this paper, we propose a deep
neural network that jointly learns informative news and readers' interests into
a unified framework. We learn the news representation (features) from the
headlines, snippets (body) and taxonomy (category, subcategory) of news. We
learn a reader's long-term interests from the reader's click history,
short-term interests from the recent clicks via LSTMSs and the diversified
reader's interests through the attention mechanism. We also apply different
levels of attention to our model. We conduct extensive experiments on two news
datasets to demonstrate the effectiveness of our approach.
