Special Issue on News Personalization and Analytics - User Modeling and User Adapted Interaction: The Journal of Personalization Research (UMUAI)
UMUAI web site: http://www.umuai.org/
Call For Papers
Scope of the Special Issue
The rapid development of Internet-based technologies has shifted news consumption models from reading physical newspapers to visiting online news websites, social media platforms, and news aggregators. Personalized news delivery services and interfaces alleviate information overload and adapt news content for individuals building on users' explicit and latent interests. However, there are still many research challenges in this area which require a deeper analysis of both the user, the content, and their relationships, such as the context-awareness, the (sequential) user behavior modeling, the explainability, diversity, and fairness of news recommender systems as well as the big data management for online news services. The highly dynamic and diverse nature of social network platforms adds to these challenges further complexity. Moreover, fake news, disinformation, echo chambers, or biased news framing may hurt the user experience and lead to a poor news ecosystem. Furthermore, news personalization can provide voters with skewed signals featuring own-party bias and affect political actions, resulting in unhealthy outcomes such as increased polarisation. These issues need attention both from a technical and a social perspective to understand and develop solutions for the societal challenges of news personalization. Lastly, considering the complex relationships among various news entities and the special properties of news articles, such as short shelf lives, continuous, large-volume and high-velocity, effective news analysis remains an important and challenging research problem.
This special issue of User Modelling and User-Adapted Interaction aims at presenting recent progress and developments of efficient user modeling and advanced machine learning techniques in various aspects of news personalization and analytics. We invite researchers and practitioners to contribute high-quality articles focusing on the following topics.
TopicsThe topics of interest for this special issue include (but are not limited to):
- Context-aware news recommender systems
- News recommendation in social media
- Multi-modal news recommendation
- User behavior analysis and user interest modeling in the news domain
- User modeling and user profiling
- Applications of data mining for personalized search and navigation
- Personalized news user interface and visualization
- Diversity and multiperspectivity in news personalization and recommendation
- News semantics and ontologies
- Adaptive and personalized news summarization, categorization, and opinion mining
- Social Graph and heterogeneous network analysis
- User segmentation and community discovery
- Big data technologies for news streams
- News framing research
- Automated news generation
- News political leaning and tone
Psychological, Societal, and Ethical Aspects of News Personalization Systems
- Privacy and security issues
- Clickbait, fake news, and misinformation detection
- Diversity and fairness of news search/recommendation
- Bias in online news
- Transparency and explainability
- Emotion and cognition in news reception
Paper Submission and Review Process
The submission process is organized in the following steps:
- Abstract Submission:
The goal is to pre-screen submissions for topical fit. Extended abstracts (up to three pages in journal format, not counting references) should be submitted through EasyChair: https://easychair.org/conferences/?conf=umuaisinpa2022.
- Full Manuscript Submission:
After abstract pre-screening, the authors of selected papers will be asked to submit a complete version of the paper through UMUAI’s submission system.
- Abstract Submission: August 15, 2022
- Abstract notification for authors: September 15, 2022
- Initial paper submission: November 15, 2022
- Authors notification: February 1, 2023
- Revised versions due: April 1, 2023
- Final notification: May 15, 2023
- Camera-ready version due: June 15, 2023
- Özlem Özgöbek, Norwegian University of Science and Technology, Norway. Özlem works as an associate professor at the Department of Computer Science at NTNU. Her research focuses on recommender systems, privacy issues in recommender systems and disinformation detection for online news. She is a co-founder of Norwegian Big Data Symposium (NOBIDS) and actively involved in organizing INRA workshop series since 2014.
- Peng Liu, Norwegian University of Science and Technology, Norway. Peng Liu works as a postdoctoral fellow at the Norwegian Research Center for AI Innovation (NorwAI) at NTNU. His research focuses on natural language processing and recommender systems. He has conducted research in the areas of temporal data mining and user behavior analysis in news recommender systems.
- Andreas Lommatzsch, Berlin Institute of Technology, Germany. Andreas Lommatzsch works as a senior researcher and director of the Application Center “Data Analytics” at the Distributed Artificial Intelligence Lab (DAI-Labor) at the TU Berlin. His research focuses on distributed knowledge management and machine learning algorithms. His primary interests lie in the areas of recommendations based on data-streams and context-aware meta-recommender algorithms. He co-organizes the NewsImages@MediaEval challenge focusing on recommender algorithms for news portals.
- Jürgen Ziegler, University of Duisburg-Essen, Germany. Jürgen Ziegler is a full professor in the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen where he directs the Interactive Systems Research Group. His main research interests lie in the areas of human-computer interaction, human-AI cooperation, recommender systems, information visualization, and health applications. Among many other scientific functions he is currently editor-in-chief of i-com - Journal of Interactive Media and chair of the German special interest group on User-Centred Artificial Intelligence.
- Benjamin Kille, Norwegian University of Science and Technology, Norway. Benjamin Kille works as a postdoctoral fellow at the Norwegian Research Center for AI Innovation (NorwAI) at NTNU. His research focuses on recommender systems, natural language processing, and machine learning. He has co-organized a series of workshops (INRA) and competitions (NewsREEL, NewsImages@MediaEval) in the field of news recommendation.
- Zhixin (Giselle) Pu, Penn State University, USA. Zhixin Pu works as a Ph.D. student and researcher at the Donald P. Bellisario College of Communications in the Pennsylvania State University. Her research examines the interplay between people, technology, and society. She employs interdisciplinary thinking to explore the roles of emotion and cognition in recommender systems using computational methods. Her work includes serendipity and information overload in news recommender systems.