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

Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System