Program

The workshop will be held as a half-day workshop on the morning of June 08, 2026, at ACM UMAP 2026

Keynote Speaker: Ricardo Baeza-Yates

Brief Biography

Ricardo Baeza-Yates is a part-time professor at KTH Royal Institute of Technology in Sweden, and also holds part-time positions at Universitat Pompeu Fabra in Barcelona and at the University of Chile in Santiago. He is additionally affiliated with Chalmers University of Technology, the University of Gothenburg, and the University of Waterloo, and is a visiting professor at Northeastern University’s Silicon Valley campus. He was previously Director of Research at the Institute for Experiential AI at Northeastern University (2021-2025), Vice President of Research at Yahoo Labs (2006-2016), and CTO of NTENT (2016-2020). He is co-author of the best-selling textbook Modern Information Retrieval (Addison-Wesley, 1999; 2nd ed., 2011), winner of the ASIST 2012 Book of the Year Award. Ricardo Baeza-Yates is one of the leading global experts in Responsible AI, with significant contributions to web search, data mining, information retrieval, and algorithmic bias. He is an ACM Fellow and IEEE Fellow, and a member of several international academies, including Academia Europaea and the Chilean Academy of Engineering. He earned his Ph.D. in Computer Science from the University of Waterloo in 1989.

Keynote Speaker: Lina Yao

Brief Biography

Lina Yao is a Senior Principal Research Scientist and Science Lead (Research Manager) @ CSIRO’s Data61 (https://people.csiro.au/y/l/lina-yao). She strives to develop generalizable and explainable data-efficient data mining, machine learning, and deep learning algorithms—as well as designing systems and interfaces—to enable novel ways of human-AI interactions and cooperations, including an improved understanding of challenges such as robustness, trust, explainability, and resilience that improve human-autonomy partnership. Particularly, here research interest includes Few-Shot Learning, Zero-Shot Learning, Deep Reinforcement Learning, Meta-Learning, Neural Process, Self-supervised Learning, Graph Neural Networks and their applications in a broad range of applications in Recommender Systems, Computer Vision, Brain Computer Interface, Intelligent Transportation System, and Internet of Things. Here research is motivated by, and contributes to, various applications in Healthcare Informatics, Cyber Security, Transportation, Defence, Industry 4.0 and FinTech.

Accepted Contributions

  • Toward Agentic Reconciliation: The Case for Multi-Stakeholder Negotiation in Tourism Recommender Systems (143-147)
    Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah, Wolfgang Wörndl, Yashar Deldjoo
  • Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems (148-159)
    Andrea Forster, Peter Müllner, Denis Helic, Elisabeth Lex, Dominik Kowald
  • Sustainable Extractive Question Answering for Resource-Constrained Personalized Document Assistants (160-168)
    Sayali Nitin Doifode
  • SciTeller: An LLM-Based Framework for Persona-Adaptive Scientific Storytelling (169-188)
    Alex Argese, Andrea Sillano, Pasquale Lisena, Raphaël Troncy, Tommaso Calò, Luigi De Russis
  • A Participatory Governance Framework for Culturally Adaptive LLM Personalization: Advancing Digital Humanism Through Community-Driven Safeguards (189-198)
    Carine P. Mukamakuza, Ronald Kato