Person 1

Xavier Amatriain

Xavier Amatriain (PhD) is currently co-founder and CTO of Curai, a stealth healthcare/AI company. Previous to this, he was VP of Engineering at Quora, and Research/engineering Director at Netflix, where he led the team building the famous Netflix recommendation algorithms. Before going into leadership positions in industry, Xavier was a research scientist and research manager both in academia and industry. He was a research scientist at Telefonica Research and a research director at UCSB. With over 50 publications (and almost 3k citations) in different fields, Xavier is best known for his work on machine learning in general, and recommender systems in particular. He has lectured at different universities both in the US and Spain and is frequently invited as a speaker at conferences and companies. His 4-hour lecture on Recommender Systems for the 2014 Machine Learning Summer School has had over 15k views on Youtube. Xavier has been closely involved with the ACM Recsys conference since its first edition. Among several other roles, he was general co-Chair for the conference in 2010.

Person 2

Alejandro Bellogín

Alejandro Bellogín is Lecturer at Universidad Autónoma de Madrid (UAM). Previously, Alejandro held a post-doctoral research grant associated to the Centrum Wiskunde & Informatica (CWI), where he worked with Prof. Arjen de Vries. In November 2012, he defended my PhD dissertation under the supervision of Prof. Pablo Castells and Dr. Iván Cantador at Universidad Autónoma de Madrid. His research is focused on recommender systems, in particular, adaptations from the information retrieval area, such as performance prediction techniques, evaluation methodologies, and probabilistic models.

Person 3

Shlomo Berkovsky

Shlomo Berkovsky is a Principal Researcher at CSIRO. He is the leader of the Interactive Behavior Analytics team and works on projects on personalized information delivery, human-machine trust, and cybersecurity. Shlomo's broad research areas include user modeling, personalization, recommender systems, and persuasive technologies. He is the author of more than 100 papers published in journals, books, and conference proceedings. His works have won the Best Paper Award of the AH-2006 and HIC-2016 conferences and 4 iAward prizes. Shlomo served on the organizing committee of 9 conferences (as the general, PC, workshops, tutorials, or doctoral consortium chair) and also chaired 14 workshops. In the coming years he will chair the RecSys-2017 and IUI-2018 conferences. Shlomo has presented 5 invited talks and 9 tutorials on the areas of his research, including at IJCAI, WWW, KDD, and WISE conferences. He recently presented a course on personalization and recommendation technologies at a summer school on Information Retrieval.

Person 5

Balázs Hidasi

Balázs Hidasi is the Head of Data Mining and Research in Gravity Research and Development. He started working at Gravity in 2010 as a Data Mining Researcher and was appointed to lead the research activities of the company in 2015. Simultaneously he began working on his PhD at the Budapest University of Technology and got his summa cum laude PhD in 2016. His research is (mostly) focused on novel collaborative filtering machine learning methods with the aim of making recommendations more accurate. This covers a wide range topics from matrix and tensor factorization to neural networks. He is one of the pioneers and evangelists of deep learning methods in the recommender systems community. He published a seminal paper on using deep learning for session based recommendations in 2015 and he is the main organizer of the Deep Learning for Recommender Systems (DLRS) workshop held in conjunction with RecSys in 2016 and 2017. He has published his research in prestigious journals, such as the Springer journal Data Mining and Knowledge Discovery; has presented at conferences such as ICLR or RecSys; and has participated in EU funded research projects with recommendation aspects such as CrowdRec or enCOMPASS.

Person 6

Marco de Gemmis

Marco de Gemmis is Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy, where he received his PhD in Computer Science in 2005. His primary research interests include content-based recommender systems, natural language processing, information retrieval, text mining, and in general personalized information filtering. He authored over 100 scientific articles published in international journals and collections, proceedings of international conferences and workshops, and book chapters. He was program committee member for international conferences, including: ACM Recommender Systems; User Modeling, Adaptation and Personalization (UMAP), and served as a reviewer for international journals, including: User Modeling and User Adapted Interaction; ACM Transactions on Internet Technologies. He was invited speaker at several universities, including: University of Roma 3, University of Basque Country San Sebastian, University of Cagliari, University of Milano-Bicocca, University of Naples Federico II, and at Workshop on Semantics-Enabled Recommender Systems at ICDM 2016.

Person 6

Dietmar Jannach

Dietmar Jannach is a full professor of computer science at TU Dortmund, Germany, where he heads the e-service research group of the department. His research focus is on applying artificial intelligence technology to practical application with a special focus on recommender systems. In 2003, he co-founded a technology startup company that focused on adaptive interactive selling solutions. Dietmar Jannach is the author of numerous scientific publications in different fields of AI and one of the authors of the textbook "Recommender Systems – An Introduction".

Person 7

Bart Knijnenburg

Bart Knijnenburg is an Assistant Professor of Human-Centered Computing at the Clemson University School of Computing. Bart received his PhD from the University of California, Irvine, where his research focused on personalized privacy decision support systems and the user-centric evaluation of recommender systems. At Clemson, he is the co-director of the Humans-And-Technology Lab (HATLab). He has received over $1M of funding from NSF and the Department of Defense, and he works on user-centric recommender system research in close collaboration with several companies and institutes. Bart is the author of more than 50 papers published in journals, books, and conference proceedings. His paper on Explaining the User Experience of Recommender Systems has consistently been among the top-3 most downloaded papers of the UMUAI journal since its publication.

Person 6

Pasquale Lops

Pasquale Lops is Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He received the Ph.D. in Computer Science from the University of Bari in 2005 with a dissertation on “Hybrid Recommendation Techniques based on User Profiles”. His research interests include recommender systems, machine learning and user modelling. He authored over 200 articles published in international journals, international collections, proceedings of national and international conferences and workshops, and book chapters. He participated in more than 20 funded research projects. He was Area Chair of User Modelling for Recommender Systems at the International Conference on User Modeling, Adaptation and Personalization (UMAP) 2016, and Senior Program Committee member of the ACM Conference on Recommender Systems since 2014. He co-organized more than 20 workshops related to user modeling and recommender systems. He was a keynote speaker at the 1st Workshop on New Trends in Content-based Recommender Systems (CBRecSys) at RecSys 2014; at the Social and Semantic aspects of recommender systems - A workshop on advances in recommender systems (RecSoc 2015). He gave the interview “Beyond TF-IDF” in the Coursera MOOC on Recommender Systems.

Person 6

Judith Masthoff

Professor Judith Masthoff is a chair in Computing Science at the University of Aberdeen. Her research is in personalization and intelligent user interfaces. She has authored many papers on recommender systems, particularly on group recommender systems and explaining recommendations, with over 1500 citations to this work. She is Editor in Chief of the User Modeling and User-Adapted Interaction journal, and a director of User Modeling Inc., the professional association of user modeling researchers.

Person 6

Cataldo Musto

Cataldo Musto is Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He completed his Ph.D. in 2012 with a dissertation on "Enhanced Vector Space Models for Content-based Recommender Systems". His research focuses on the adoption of techniques for semantic content representation in recommender system, user modeling, and intelligent adaptive platforms. He was an invited speaker at the workshop on Semantic Adaptive and Social Web (SASWeb) at UMAP 2012 and at the first workshop on Financial Recommender Systems (FINREC 2015). He gave a tutorial at UMAP 2016, and he has published over 50 papers and served as reviewer or co-reviewer in the Program Committee of several conferences in the area as ACM Recommender Systems, ECIR, UMAP and WWW.

Person 6

Fedelucio Narducci

Fedelucio Narducci is a PostDoc Research Fellow at the Department of Computer Science, University of Bari Aldo Moro, Italy. He is also member of the SWAP (Semantic Web Access and Personalization) research group of University of Bari Aldo Moro. His primary research interests lie in the areas of machine learning, recommender systems, user modeling, and personalization. He completed his Ph.D. in 2012 with a dissertation on Knowledge-enriched Representations for Content-based Recommender Systems. From April 2012 to July 2014 he worked as a PostDoc Researcher at the university of Milano-Bicocca on the SMART (Services & Meta-services for smART eGovernment) project with a specific focus on cross-language information retrieval and filtering. He has published more than 50 papers and served as a reviewer and co-reviewer for international conferences and journals in the areas of recommender systems, user modeling, and personalization.

Person 7

Alan Said

Alan Said is Lecturer at The University of Skövde. He holds a PhD from Technishe Universität Berlin. Prior to joining the University of Skövde, Alan held various positions in industry and academia; He was a machine learning engineer at Recorded Future (2014-2016). He was Senior Researcher (2014) in the Multimedia Computing research group at Delft University of Technology. He was awarded an MSCA Alain Bensoussan ERCIM Fellowship at Centrum Wiskunde & Informatica (2013-2014). Alan's research spans the fields of user modeling, personalization, recommender systems, evaluation, and reproducibility. He frequently serves on Program and Organization committees of top venues and journals such as ACM RecSys, WWW, ACM CIKM, ACM UMAP, ACM IUI, UMUAI, TWeb, TKDD, etc.

Person 8

Giovanni Semeraro

Giovanni Semeraro is Full Professor of Computer Science at University of Bari Aldo Moro, Italy, and teaches “Intelligent Information Access and Natural Language Processing”, and “Programming languages”. He leads the Semantic Web Access and Personalization (SWAP) research group “Antonio Bello” at the Department of Computer Science of the University of Bari Aldo Moro, Italy. He was one of the founders of AILC (Italian Association for Computational Linguistics) and he is on the Board of Directors. From 2006 to 2011 he was on the Board of Directors of AI*IA (Italian Association for Artificial Intelligence). He has been a visiting scientist with the Department of Information and Computer Science, University of California at Irvine, in 1993. From 1989 to 1991, he was a researcher at Tecnopolis CSATA Novus Ortus, Bari, Italy. His research interests include recommender systems; machine learning; AI and language games; intelligent information mining, retrieval, and filtering; semantics and social computing; natural language processing; the Semantic Web; personalization. He served as Program Co-chair of ACM RecSys 2015 and as General Co-chair of UMAP 2013, IIR 2013, SemExp 2012, IIR 2012, IIA 2008, AI*IA 2008, SWAP 2007, CILC 2006, and as Program Co-chair of Decisions@RecSys 2013 & 2012, DART 2013, 2012 & 2011, RSmeetDB@DEXA 2013 & 2012, SeRSy@RecSys 2013 & SeRSy@ISWC 2012, DEMRA@UMAP 2011, SPIM@ISWC 2011, EC-Web 2010, SWAP 2010, Web Mining 2.0@ECML/PKDD 2007, ISMIS 2006, WebMine@ECML/PKDD 2006, IEA-AIE 2005.

Person 9

Guy Shani

Guy Shani is a Full Professor in the department of Software and Information Systems, at the Ben Gurion University. Previously, he was a researcher at Microsoft research, in the Machine Learning and Statistics group, working on various recommendation systems applications. He is interested in recommender systems research, mostly focusing on novel applications of machine learning, user interfaces for recommender systems, and the evaluation of recommendations. In addition, he is also interested in automated planning under partial observability and uncertainty, and in applied machine learning in agriculture.

Person 10

Panagiotis Symeonidis

Panagiotis Symeonidis received a Bachelor (BA) in Applied Informatics from Macedonia University of Greece in 1996. He also received a Master diploma (MSc) in Information Systems from the same University in 2004. He received his PhD in Web Mining and Information Retrieval for Personalization from the Department of Informatics in Aristotle University of Thessaloniki, Greece in 2008. His research interests include web mining (usage mining, content mining and graph mining), information retrieval, collaborative filtering, recommender systems, social media in Web 2.0 and online social networks. He is the co-author of 3 international books, 1 Greek book, 4 book chapters, 18 journal publications and 29 conference/workshop publications. His articles have received more than 1400 citations from other scientific publications.

Person 11

Marko Tkalčič

Marko Tkalčič is Assistant Professor at the Faculty of Computer Science at the Free University of Bozen-Bolzano, Italy. He received his PhD from the University of Ljubljana in 2011. After a postdoc at the University of Ljubljana, he worked as postdoc at the Johannes Kepler University in Linz, Austria from 2013 to 2015. From 2016 he is with the Free University of Bozen-Bolzano. His research explores ways in which psychologically-motivated user characteristics, such as emotions and personality, can be used to improve personalized systems. It employs methods such as user studies and machine learning. Dr. Tkalčič has published in prestigious journals, such as Elsevier Information Sciences, Springer UMUAI and most recently IEEE Transactions on Affective Computing. He has presented at venues, such as RecSys and UMAP. Recently he edited the book Emotions and Personality in Personalized Systems with Springer. He is active in organizing conferences (RecSys 2017, UMAP 2017) and workshops (EMPIRE, SOAP, HUMANIZE), editing special issues, and reviewing for prestigious journals, conferences and grant bodies.