Bracha Shapira is an Associate Professor of Information Systems Engineering at Ben-Gurion University of the Negev and the head of the department. Her main areas of interest are Information Retrieval, Recommender Systems, Cyber Security, and data mining. Prof. Shapira established the Information Retrieval Research Laboratory where she leads research projects related to personalization of search engines and recommender systems. She also leads research projects at the Deutsche Telekom Innovative laboratories at Ben-Gurion University. Bracha Shapira is the author of over 90 peer reviewed papers in leading journals, conference proceedings, patents, and book chapters. In addition, Prof. Shapira has co-edited the Recommender Systems Handbook.
Lior Rokach is an Associate Professor of Information Systems Engineering at Ben-Gurion University of the Negev. His main areas of interest are Machine Learning, Recommender Systems, Cyber Security, and Information Retrieval. Prof. Rokach established the Machine Learning Research Laboratory which promotes innovative adaptations of machine learning methods in various domains. He is the author of over 150 peer reviewed papers in leading journals, conference proceedings, patents, and book chapters. In addition, Prof. Rokach has also authored six books and co-edited the Data Mining and Knowledge Discovery Handbook and the Recommender Systems Handbook. Prof. Rokach received his PhD from Tel Aviv University in 2004.
Francesco Ricci is an associate professor of computer science
at the Free University of Bozen-Bolzano, Italy. His current research interests include recommender systems, intelligent interfaces, mobile systems, machine learning, case-based reasoning, and the applications of ICT to tourism. He has published more than one hundred of academic papers on these topics. He is the editor in chief of the Journal of Information Technology & Tourism and on the editorial board of User Modeling and User Adapted Interaction. He served as president of the steering committee of the ACM Conference on Recommender Systems.
