Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System
This addresses the problem of improving news recommendation quality for readers by incorporating diversity alongside accuracy, though it appears to be an incremental improvement over existing methods.
The paper tackles the challenge of balancing accuracy and diversity in news recommender systems by proposing a deep neural network that jointly learns news representations and reader interests from multiple sources. Experimental results on two datasets demonstrate the effectiveness of their approach, though specific performance numbers are not provided in the abstract.
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.