Christine Bauer is a Senior Postdoc Researcher at Johannes Kepler University (JKU) Linz, Austria. Her research focuses on the human perspective in the interaction with context-adaptive, intelligent systems. Currently, she lays a focus on music recommender systems. Christine's research and teaching activities are driven by her interdisciplinary background. She holds a Doctoral degree in Social and Economic Sciences, a Master degree in Business Informatics, and a Diploma degree in International Business Administration. In addition, she pursued studies in jazz saxophone. Christine is an experienced teacher in a wide spectrum of topics in computing and information systems, taught across 10 institutions. She engages in mentoring for initiatives such as Women in Music Information Retrieval and Austrian HCI Networking. Before joining JKU with her prestigious Elise Richter grant, she researched at WU, Austria, University of Cologne, Germany, and the E-Commerce Competence Center, Austria. In 2013 and 2015, she was Visiting Fellow at the Ubicomp Lab at Carnegie Mellon University, Pittsburgh, PA. Christine has authored more than 85 papers in refereed journals and conference proceedings, and holds several best paper awards as well as awards for her reviewing activities.
Amine Benhalloum is a Senior Machine Learning Engineer at Criteo, working on building large scale representation learning (mostly in Tensorflow these days) and retrieval systems for recommendation, applying Deep learning to personalize billions of daily display ads, reaching billions of users and connecting them with millions of products, his areas of expertise are: large scale machine learning, natural language processing, information retrieval and data intensive systems. Before joining Criteo, Amine worked on a variety of topics ranging from Deep Learning for Natural Language processing to fraud detection. He holds a master's degree in Applied Mathematics.
Robin Burke is Professor and Chair of the Department of Information Science at the University of Colorado, Boulder. He has been engaged in recommender systems research for more than 20 years, publishing more than 70 peer-reviewed articles on this and related topics, and is the current chair of the steering committee for the ACM Recommender Systems conference. Most recently, his research has focused on multistakeholder and fairness-aware recommendation. Prior to CU Boulder, Prof. Burke spent 16 years at DePaul University's School of Computing, where he co-led the Center for Web Intelligence. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.
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 personalized information filtering. He authored over 150 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 several international journals, including: User Modeling and User Adapted Interaction; ACM Transactions on Internet Technologies. He served as Doctoral Consortium Chair and Workshop Chair at UMAP and organized several international workshops. He was editor of the book "Emotions and Personality in Personalized Services - Models, Evaluation and Applications", Springer, 2016. He was invited speaker at the 1st ACM Summer School on Recommender Systems in Bolzano (2017) and at the Workshop on Semantics-Enabled Recommender Systems (IEEE ICDM), 2016.
David Graus is lead data scientist for the SMART Journalism and SMART Radio projects at the FD Mediagroep, the leading financial economic news provider in the Netherlands. He works on bringing AI to media through algorithmic personalization and summarization. David obtained his PhD degree at the University of Amsterdam (UvA) on semantic search and computational methods for analyzing large-scale textual digital traces in the context of e-discovery and digital forensics. Going further back, he has a background in the media, and worked as an editor at a Dutch public broadcaster for online and radio programs.
Dietmar Jannach is a full professor of Information Systems at AAU Klagenfurt, Austria. In his research, he focuses on the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In the last years, Dietmar Jannach worked on various practical aspects of recommender systems. He is the main author of the first text book on the topic published by Cambridge University Press in 2010 and was the co-founder of a tech startup that created an award-winning product for interactive advisory solutions.
Peter Knees is Assistant Professor of the Faculty of Informatics, TU Wien, Austria. For over a decade he has been an active member of the ISMIR community, reaching out to the related areas of multimedia, text IR, and recommender systems. Apart from serving on the program committees of major conferences in these fields, he has organized several workshops on topics of media retrieval. He is an experienced teacher of graduate-level courses on recommender systems, information retrieval, and data science and has given tutorials and courses on music information retrieval and recommendation at RecSys, SIGIR, ECIR, ISMIR, RuSSIR, the Indonesian Summer School on MIR, and the Sound and Music Computing Summer School.
Joseph A. Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor in the Department of Computer Science and Engineering, and Associate Dean for Research in the College of Science and Engineering at the University of Minnesota, where he formerly led the GroupLens Center for Social and Human-Centered Computing. His research addresses a variety of human-computer interaction issues, including recommender systems and social computing. He is best known for his work in collaborative filtering recommenders (the GroupLens project won the ACM Software Systems Award and one of its papers was recognized with the Seoul Test of Time Award), and for the creation of the MovieLens recommender system and datasets. Dr. Konstan received his Ph.D. from the University of California, Berkeley in 1993. He is a Fellow of the ACM, IEEE, and AAAS and a member of the CHI Academy. He chaired the first ACM Conference on Recommender Systems in 2007, and has served on its steering committee since its inception.
Pasquale Lops is Associate 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 and user modelling, with a specific focus on the adoption of techniques for semantic content representation. He authored over 200 articles published in international journals, international collections, proceedings of national and international conferences and workshops, and book chapters, and he is one of the authors of the textbook "Semantics in Adaptive and Personalized Systems: Methods, Tools and Applications", under publication by Springer. He was Area Chair of User Modelling for Recommender Systems at UMAP 2016, and co-organized more than 20 workshops related to user modeling and recommender systems. He gave a tutorial on "Semantics-Aware Techniques for Social Media Analysis, User Modeling, and Recommender Systems" at UMAP 2016 and 2017, he was a speaker at the 1st ACM Summer School on Recommender Systems in Bolzano (2017), and he was a keynote speaker at the 1st Workshop on New Trends in Content-based Recommender Systems (CBRecSys) at RecSys 2014. Finally, he gave the interview “Beyond TF-IDF” in the Coursera MOOC on Recommender Systems.
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, UMAP 2017, ESWC 2017 and he has published more than 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.
Fedelucio Narducci is a PostDoc Researcher at the Department of Computer Science, University of Bari Aldo Moro, Italy. He is also a 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. His last works are most focused on Conversational Recommender Systems offering a Natural Language Interaction. Fedelucio, besides being the author of several scientific publications in different AI areas, is one of the authors of the textbook "Semantics in Adaptive and Personalized Systems" to be shortly published by Springer. He served as a reviewer and co-reviewer for international conferences and journals in the areas of recommender systems, user modeling, and personalization.
Julia Neidhardt is a researcher at the E-Commerce Research Unit at TU Wien, Austria. She holds a Master's degree in mathematics from the University of Vienna and a PhD in computer science from TU Wien. Her research focuses on modeling and predicting complex human behavior, user opinions, preferences and social relations as well as their dynamics in digital-enabled environments. In recent projects, she studied social influence mechanism in online communities, the diffusion of topics, opinions and sentiments, social media-based event prediction, team performance, news recommender systems as well as picture-based travel recommender systems. Since 2013, she has been a regular visiting researcher at the Science of Networks in Communities (SONIC) research group at Northwestern University, USA. Her work is published in internationally highly renowned conferences and journals including Nature Human Behaviour.
Daan Odijk is the lead data scientist at RTL, the largest commercial commercial broadcaster in the Netherlands and part of Bertelsmann. In 2016, he obtained his PhD on search algorithms for news. Subsequently, he joined journalism start-up Blendle, leading the personalization team. At RTL, Daan leads data scientists and engineers, delivering data-powered products across RTL, including personalization for RTLNieuws and Videoland. At Bertelsmann, Daan leads an AI expert group to foster AI applications across one of the world's largest mass media companies.
Giovanni Semeraro is full professor of computer science at University of Bari Aldo Moro, Italy, where he teaches “Intelligent Information Access and Natural Language Processing”, and “Programming languages”. He leads the Semantic Web Access and Personalization (SWAP) “Antonio Bello” research group. In 2015 he was selected for an IBM Faculty award on Cognitive Computing for the project “Deep Learning to boost Cognitive Question Answering”. He was one of the founders of AILC (Italian Association for Computational Linguistics) and he was on the Board of Directors till 2018. 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 machine learning; AI and language games; recommender systems; user modelling; intelligent information mining, retrieval, and filtering; semantics and social computing; natural language processing; the semantic web; personalization. He has been the principal investigator of University of Bari in several European, national, and regional projects. He is author of more than 400 publications in international journals, conference and workshop proceedings, as well as of 2 books, including the textbook "Semantics in Adaptive and Personalized Systems: Methods, Tools and Applications", under publication by Springer. He regularly serves in the PC of the top conferences in his areas and is Program Co-Chair of CLiC-it 2019. Among others, he served as Program Co-chair of CLiC-it 2016, ACM RecSys 2015 and as General Co-chair of UMAP 2013. From 2013, he is the coordinator of the 2nd Cycle Degree Program in Computer Science at University of Bari. He is the coordinator of the 1st edition of the Master in Data Science at University of Bari. He is a member of the Steering Committee of the National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS) of the National Interuniversity Consortium for Informatics (CINI).
Christoph Trattner is an Associate Professor at the University of Bergen in the Information Science & Media Studies Department. Previously, he was an Asst. Prof. at MODUL University Vienna in the New Media Technology Department. He also founded and led the Social Computing department at the Know-Center, Austria’s research competence for data-driven business and big data analytics. He holds a Ph.D. in Computer Science and Telematics from Graz University of Technology (Austria). Christoph’s research background includes Applied Machine Learning, Predictive Modeling, Recommender Systems, Social Networks Analysis, Human Computer Interaction and Data Science in particular. He is leading an international research effort that tries to understand, predict and change online food preferences to tackle health-related food issues such as diabetes or obesity. Since 2010, he published two books and over 90 scientific articles in top conferences and journals including, e.g., JASIST, UMUAI, TiiS, ComCom, EPJ Data Science, WWW, ICWSM. He holds several Best Paper/Poster Awards and Nominations, including, the Best Paper Award Honorable Mention in 2017 at the prestigious WWW conference series.
Flavian Vasile is part of the Criteo AI Lab where he works as the ML Recommendations Solutions Architect, with his main focus being on the development of Deep Learning-based Recommendation Systems and on introducing aspects of Causal Inference to Recommendation. Before joining Criteo, he worked as a Senior Researcher in the Twitter Advertising Science team; before that, in the Yahoo! Research Lab where he mostly focused on Content Understanding problems. His current research interests include Deep Sequential Models for Recommendation and understanding Recommendation as a decision-making system with reward uncertainty. Among his recent research publications, the work on Causal Embeddings for Recommendation received the best paper award at RecSys 2018 and he is the co-organizer of the Workshop on Offline Evaluation for Recommender Systems in conjunction with ACM RecSys 2019.
Martijn Willemsen is an expert on human decision making in interactive systems. He is working as an associate professor in the Human-Technology Interaction group of Eindhoven University of Technology (The Netherlands) and is the principle investigator of the recommender LAB at the Jheronimus Academy of Data Science (jads.nl). He is part of the core team of the Customer Journey Research Program in the Data Science Center Eindhoven (DSC/e) and is teaching in the BSc and MSc data science programs of JADS, in the BSc. Program Psychology and Technology and in the master program Human-Technology Interaction. His primary interests lie in the understanding of cognitive processes of decision making by means of process tracing and in the application of decision making theory in interactive systems such as recommender systems. He is also an expert on user-centric evaluation of recommender systems. Recent research topics include interactive recommender systems that help people to improve their lives and well-being: for example in saving energy, improve health or finding new tastes in music using a Spotify-based genre exploration app.