Group Recommender Systems

Alexander Felfernig is a full professor at the Graz University of Technology (Austria) since March 2009 and received his PhD in Computer Science from the University of Klagenfurt. He directs the Applied Software Engineering (ASE) research group. His research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and machine learning. In these areas, he is engaged in national research projects as well as in a couple of European Union projects. Alexander Felfernig has published numerous papers in renowned international conferences and journals (e.g., AI Magazine, Artificial Intelligence, IEEE Transactions on Engineering Management, IEEE Intelligent Systems, Journal of Electronic Commerce, and User Modeling and User-Adapted Interaction) and is a co-author of the book on "Recommender Systems" published by Cambridge University Press. He also acted as an organizer of internationalconferences such as the ACM International Conference on Recommender Systems,the International Symposium on Methodologies for Intelligent Systems, and the ACM International Systems and Software Product Line Conference, and is a member of the Editorial Board of Applied Intelligence and the Journal of Intelligent Information Systems. With his research, he contributed to the development of commerical decision support and recommender systems.Martin Stettinger studied Software Development and Business Economics at Graz, University of Technology and graduated in 2013 as MSc. with honors. He received his PhD in Computer Science in 2016 with honors. At the moment, he is Senior Researcher at the Institute of Software Technology at Graz University of Technology, and co-founder and CTO of the 2013 founded SelectionArts Intelligent Decision Technologies Inc. The research focus of Martin Stettinger is in the field of recommender systems and includes recommendation-supported e-Learning, human decision making and highly personalized software systems. These topics include recommendation technologies for group decision tasks, different aspects of human learning and decision making behaviour as well as different methods of knowledge acquisition. He published over 90 publications at international workshops, conferences, and journals. He has long-term experience in software projects and special expertise in the areas of mobile devices, Web-programming, and Web-applications.Ludovico Boratto is a researcher at the Department of Mathematics and Computer Science of the University of Cagliari (Italy). His research interests focus on recommender systems and their impact on the different stakeholders, both considering accuracy and beyond-accuracy evaluation metrics. He has authored more than 60 papers and published his research in top-tier conferences and journals.  His research activity also brought him to give talks and tutorials at top-tier conferences and research centers (Yahoo! Research). He is editor of the book “Group Recommender Systems: An Introduction”, published by Springer. He is an editorial board member of the “Information Processing & Management” journal (Elsevier) and “Journal of Intelligent Information Systems” (Springer), and guest editor of several journals’ special issues. He is regularly part of the program committees of the main Web conferences, where he received four outstanding contribution awards. In 2012, he got his Ph.D. at the University of Cagliari (Italy), where he was a research assistant until May 2016. From May 2016 to April 2021, he joined Eurecat as Senior Research Scientist in the Data Science and Big Data Analytics research group. In 2010 and 2014, he spent ten months at Yahoo! Research in Barcelona as a visiting researcher. He is a member of ACM and IEEE. Marko Tkalčič is associate professor at the Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT) at the University of Primorska in Koper, Slovenia. He aims at improving personalized services (e.g. recommender systems) through the usage of psychological models in personalization algorithms. To achieve this, he uses diverse research methodologies, including data mining, machine learning, and user studies. He is editorial board member of the Springer UMUAI journal. He served as Program Chair at the ACM UMAP 2021 conference.

“Group recommender systems: an introduction serves as an essential reference for both researchers and practitioners in the field. It combines technical details with practical applications and psychological insights, making it a well-rounded resource for anyone interested in the development and implementation of group recommender systems. It offers a rich, multi-dimensional exploration of its subject matter, and promises to be a key resource for anyone interested in the development and application of recommender systems, especially those focused on group settings.” (Wael Badawy, Computing Reviews, December 29, 2024)

• Introduction.- Group Recommendation Techniques.- Decision Tasks and Basic Algorithms.- Algorithms for Group Recommendation.- Evaluating Group Recommender Systems.- Part 2. Group Recommender User Interfaces.- Group Recommender Applications.- Handling Preferences.- Explanations for Groups.- Part 3. Group Decision Processes.- Further Choice Scenarios.- Biases in Group Decisions.- Personality, Emotions, and Group Dynamics.- Conclusions.