Deep Neural Network to Tradeoff between Accuracy and Diversity in a News Recommender System

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 driven by a blend of a reader’s long-term and short-term interests. In addition, diversity is required in a news recommender system to keep the reader engaged in the reading process and get them exposed to different views and opinions. This paper proposes a deep neural network that jointly learns news and user representation in a unified framework. It learns the news representation (features) from the headlines, snippets (body) and taxonomy (category, subcategory) of news. The attention mechanism learns a reader’s long-term interests from the complete click history, short-term interests from recent clicks via LSTMs and diverse interests. 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.