The news domain is characterized by a constant flow of unstructured, fragmentary, and unreliable news stories from numerous sources and different perspectives. Finding the right information, either in terms of individual news stories or aggregated knowledge from analyzing entire news streams, is a tremendous challenge that necessitates a wide range of technologies and a deep understanding of user preferences, news contents, and their relationships.

This workshop addresses primarily news recommender systems and news analytics, with a particular focus on user profiling and techniques for dealing with and extracting knowledge from large-scale news streams. The news streams may originate in large media companies, but may also come from social sites, where user models are needed to decide how user-generated content is to be taken into account. As part of news recommendation and analytics, Big Data architectures and large-scale statistical and linguistic techniques are used to extract aggregated knowledge from large news streams and prepare for personalized access to news. Topics of interests include but are not limited to:

- News semantics and ontologies
- News summarization, classification and sentiment analysis
- Recommender systems and news personalization
- Group recommendation for news
- User profiling and news context modeling
- Real time news recommendations
- News evolution and trends
- Large-scale news mining and analytics
- Evaluation methods
- News from social media
- Big Data technologies for news streams
- News recommendation and analytics on mobile platforms
- Privacy in news recommender systems
- Gamification in news recommender systems
- News diversity
- Detection and analysis of fake and/or trusted news