ACM Europe School on
Recommender Systems 2025

Morten Arngren is Head of Data Science at Anthill Agency, leading a team developing GenAI Agents for digital advertising in the pharmaceutical sector. Previously, he led data science teams at WML, focusing on privacy-safe algorithms using Deep Learning and Bayesian models, and at Adform, creating machine learning products like Bayesian Bandits and Cross-Device Tracking. At Issuu, he built a hybrid collaborative recommender system and NLP/image-based models. Morten earned his Industrial PhD in machine learning in 2011, specializing in Bayesian models, and presented a tutorial on Bayesian A/B Testing at RecSys 2023. Outside work, he enjoys snowboarding and family life.

Francesco Barile is an Assistant Professor of Explainable Recommender Systems at Maastricht University. His research focuses on Group Recommender Systems, Multi-stakeholder Recommender Systems, and Explainable AI.

Toine Bogers is an associate professor at the IT University of Copenhagen and Chief Scientific Officer at the Pioneer Centre for AI, a leading Danish research hub for interdisciplinary AI. His research focuses on applying information access technologies, such as recommender systems and search engines, to large information collections, with recent emphasis on fair algorithmic hiring in the HR domain through nationally funded projects. With over 18 years of teaching experience, he has delivered 25+ courses to humanities and computer science students on topics like data science, analytics, and research methods. He also co-organizes the RecSys in HR workshop series, now in its 5th edition.

Professor Chen focuses on personalized conversational and explainable AI, with applications in entertainment, media, education, e-commerce, and well-being. She has authored 150+ publications in top journals (e.g., IJHCS, TOCHI, UMUAI) and conferences (e.g., SIGKDD, AAAI, SIGIR, RecSys, CHI). Her work has earned multiple awards, including RecSys’24 Best Student Paper and CHI’22 Honourable Mention. Recognized among the world’s top 2% most-cited scientists since 2021, she is an ACM Senior Member, Co-Editor-in-Chief of ACM TORS, and serves on several editorial boards and conference committees. She has also chaired ACM RecSys’23, co-chaired RecSys’20, and UMAP’18, and received prestigious teaching and supervision awards.

Dr. Yashar Deldjoo is a Senior Research Scientist and Associate Professor at the Polytechnic University of Bari, Italy. His research integrates trustworthy and responsible AI—fairness, robustness, and security—into recommender systems, with a focus on Generative AI (e.g., LLMs and agents). He studies and mitigates risks like hallucinations, randomness, and bias, proposing holistic evaluation frameworks for accurate, ethical recommendations. He co-leads the upcoming FN&TIR’25 book Next-Generation Recommender Systems under Generative AI, serves as Associate Editor for IEEE TKDE and ACM CSUR, Guest Editor for ACM TORS, and is an SPC member for SIGIR, CIKM, ECAI, The WebConf, and ACM RecSys.

Michael Ekstrand is an assistant professor of information science at Drexel University, where he leads the Impact, Novation, Effectiveness, and Responsibility of Technology for Information Access Lab (INERTIAL). His research blends human-computer interaction, information retrieval, machine learning, and statistics to try to make information access systems, such as recommender systems and search engines, good for everyone they affect. In 2018, he received the NSF CAREER award to study how recommender systems respond to biases in input data and experimental protocols and predict their future response under various technical and sociological conditions, and is co-PI on the NSF-funded POPROX project to develop shared infrastructure for user-facing recommender systems research.

Kim Falk is a Principal Recommender Engineer at DPG Media, where he works on recommender systems in news and VOD platforms. Previously, he was a staff recommender engineer at Shopify, as technical lead of the Product Recommendations team. Kim has experience in machine learning, specializing in Recommender systems within many different domains. Kim is also the author of Practical Recommender Systems.

Dietmar Jannach is a full professor of Computer Science at the Department of Artificial Intelligence and Cybersecurity at the University of Klagenfurt. His general research theme is related to the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. His core focus in the past years has been on the field of recommender systems. He is the main author of the first textbook on recommender systems, published with Cambridge University Press in 2010. Furthermore, he was a program chair of the ACM Conference on Recommender Systems, and he is a founding editor of ACM Transactions on Recommender Systems.

Mesut Kaya is a postdoctoral researcher specializing in recommender systems, with a
particular focus on their application in the human resources domain. He is currently affiliated
with the IT University of Copenhagen and Jobindex A/S, where he contributes to the
FairMatch project: a project aimed at developing job and candidate recommendation
algorithms that promote multi-sided fairness in algorithmic hiring. Prior to his current role,
Mesut developed a candidate recommendation system designed to assist recruiters in
identifying relevant candidates for job postings. He earned his PhD in Computer Science,
with his thesis focusing on diversification in recommendations. Mesut was a speaker at the
RecSys Summer School 2023, where he gave part of an earlier version of this proposed
lecture on job recommendation systems. Mesut also has experience teaching a
“Introduction to Data Science” course for master’s students. Mesut has also beena
co-organizer of the RecSys in HR workshop series (with a 5th edition at the 2025 RecSys
conference).

Dr. Aonghus Lawlor is an Assistant Professor in the School of Computer Science at University College Dublin (UCD) and a Funded Investigator at the Insight Centre for Data Analytics, one of Europe’s largest data analytics research centres. His work focuses on developing advanced AI solutions in recommender systems, medical imaging, and sports science. He has a proven track record of leading large-scale industry and EU collaborations, securing major funding, and publishing high-impact research.

Dr. Irina Nalis is a psychologist and joined the RecSys Lab as an interdisciplinary collaborator. She obtained her PhD from the University of Vienna, Department of Occupational, Economic, and Social Psychology. Her research interests are framed within the context of the Vienna Manifesto on Digital Humanism, including questions of behavior change, co-creation, decision-making, and transformative research.

Ladislav Peska is an Assistant Professor at Charles University. His research focus includes content-based video retrieval, data visualization, multi-objective recommender systems, and group recommender systems.

Barry Smyth (BSc, PhD, Hon. DTech(RGU), MRIA) is an academic and entrepreneur. He holds the DIGITAL Chair of Computer Science at University College Dublin. He is a member of the Royal Irish Academy and a Founding Director of the Insight Centre for Data Analytics. Barry’s research interests cover a broad range of topics, including artificial intelligence, case-based reasoning, and recommender systems. He has published over 400 peer-reviewed articles and received several awards for his research. Barry is also an entrepreneur. He co-founded ChangingWorlds and HeyStaks, based on research from his lab, and he currently serves as a board member and/or advisor for several AI-related companies.

Amra Delic is an Assistant Professor at the University of Sarajevo. Her research focuses on the personalized systems that support group decision-making processes by exploiting various user, group, and interrelationship features and the information about the decision-making process itself.

Dr. Reza Yousefi Maragheh received his Ph.D. from the University of Illinois, Urbana-Champaign, with a thesis focused on applied recommender systems. He is currently a Staff Data Scientist working on the Personalization Team at Walmart Labs, where he has been advancing and deploying large-scale personalized recommender systems for several years. His work blends various frameworks, such as graph-based, sequential, contextual approaches, as well as generative/agentic-AI orchestration, to improve retrieval, transparency, and business impact across billions of daily interactions. Reza’s research interests span user modeling, context-aware ranking, and agentic recommender systems, and he publishes in major ML/RecSys venues while holding multiple U.S. patents.