Accepted contributions.

Lightning Talks

• A Co-design Study for Multi-Stakeholder Job Recommender System Explanations
  Roan Schellingerhout (Maastricht University), Francesco Barile (Maastricht University) & Nava Tintarev (Maastricht University)

• A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions
  Norman Knyazev (Radboud University) & Harrie Oosterhuis (Radboud University)

• Amplifying Artists’ Voices: Item Provider Perspectives on Influence and Fairness of Music Streaming Platforms
  Karlijn Dinnissen (Utrecht University) & Christine Bauer (Paris Londron University Salzburg)

• Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study
  Ine Coppens (Ghent University), Luc Martens (Ghent University) & Toon De Pessemier (Ghent University)

• Examining the User Evaluation of Multi-list Recommender Interfaces in the Context of Healthy Recipe Choices
  Alain Starke (University of Amsterdam & University of Bergen), Edis Asotic (University of Bergen), Christoph Trattner (University of Bergen), Ellen J van Loo (Wageningen University & Research)

• Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
  Yu Liang (Jheronimus Academy of Data Science) & Martijn Willemsen (Eindhoven University of Technology & Jheronimus Academy of Data Science)

• Leveraging Sequential Episode Mining for Session-based News Recommendation
  Mozhgan Karimi (University of Antwerp), Boris Cule (Tilburg University) & Bart Goethals (University of Antwerp)

• Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design Nicolas Mattis (Vrije Universiteit Amsterdam), Philipp Masur (Vrije Universiteit Amsterdam), Judith Möller (Vrije Universiteit Amsterdam), Wouter Van Atteveldt (Vrije Universiteit Amsterdam)

• Pessimistic Decision-Making for Recommender Systems
  Olivier Jeunen (ShareChat) & Bart Goethals (University of Antwerp) 

• RankFormer: Listwise Learning-to-Rank Using Listwide Labels
  Maarten Buyl (Amazon & Ghent University), Paul Missault (Amazon) & Pierre-Antoine Sondag (Amazon)

Posters

• A Probabilistic Position Bias Model for Short-Video Recommendation Feeds
  Olivier Jeunen (ShareChat)

• An Exploration of Sentence-Pair Classification for Algorithmic Recruiting
  Mesut Kaya (Aalborg University) &Toine Bogers (IT University of Copenhagen)

• Career Path Prediction using Resume Representation Learning and Skill-based Matching
  Jens-Joris Decorte (TechWolf) & Mikkel Skovdal (TechWolf)

• Enhancing Resume Content Extraction in Question Answering Systems through T5 Model Variants
  Yuxin Luo (University of Amsterdam), Feng Lu (Randstad NV), Vaishali Pal (University of Amsterdam) & David Graus (Randstad NV)

• Extending Bayesian Personalized Ranking with Survival Analysis for MOOC Recommendation
  Alireza Gharahighehi (KU Leuven), Michala Venturini (KU Leuven), Achilleas Ghinis (KU Leuven), Frederik Cornillie (KU Leuven) & Celine Vens (KU Leuven)

• ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
  Yoosof Mashayekhi (Ghent University), Bo Kang (Ghent University), Jefrey Lijffijt (Ghent University) &  Tijl De Bie (Ghent University)

• “Tell Me Why”: Using Natural Language Justifications in a Recipe Recommender System to Support Healthier Food Choices
  Alain Starke (University of Amsterdam), Cataldo Musto (University of Bari); Amon Rapp (University of Torino), Christoph Trattner (University of Bergen), Giovanni Semeraro (University of Bari "Aldo Moro")

• VideolandGPT: A User Study on a Conversational Recommender Systems
  Dina Zilbershtein (Maastricht University) & Mateo Gutierrez Granada (RTL), Daan Odijk (RTL Nederland B.V.) & Francesco Barile (Maastricht University)

• It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
  Jin Huang (University of Amsterdam), Harrie Oosterhuis (Radboud University), Maarten de Rijke (University of Amsterdam)

Work-in-progress 

• A challenge-based survey of e-recruitment recommendation systems
  Yoosof Mashayekhi (Ghent University), Nan Li (Ghent University), Bo Kang (Ghent University), Jefrey Lijffijt (Ghent University) &Tijl De Bie (Ghent University)

• Gatekeeping in the Digital Age: Newsroom Resistance to News Personalisation
  Aina Errando (imec-SMIT, Vrije Universiteit Brussel), Heritiana Ranaivoson (imec-SMIT, Vrije Universiteit Brussel) & Adelaida Afilipoaie (imec-SMIT, Vrije Universiteit Brussel)

• Discovering the Rhythm: The Impact of Online Platform Recommender Systems on Music Discoverability
  Valdy Wiratama (imec-SMIT, Vrije Universiteit Brussel) & Heritiana Ranaivoson (imec-SMIT, Vrije Universiteit Brussel), Adelaida Afilipoaie (imec-SMIT, Vrije Universiteit Brussel) & Dongxiao Li (imec-SMIT, Vrije Universiteit Brussel)

• Diversity Contested: How the Conceptualization of Diversity in Recommender Systems Differs per Use Case
  Sanne Vrijenhoek (University of Amsterdam), Savvina Daniil (CWI) & Laura Hollink (CWI)

• Fair and Transparent Recommendations for Advertisements
  Dina Zilbershtein (Maastricht University), Francesco Barile (Maastricht University), Daan Odijk (RTL Nederland B.V.) & Nava Tintarev (Maastricht University)

• How datasets shape the way news is recommended, and how law could break dataset dependencies
  Max van Drunen (University of Amsterdam) & Sanne Vrijenhoek (University of Amsterdam)

• Intention and Behavior: A Systematic Review of Literature on Users Preferences in Recommendation Systems
  Dongxiao Li (imec-SMIT, Vrije Universiteit Brussel) & Annelien Smets (imec-SMIT, Vrije Universiteit Brussel)

• Assessing the potential of large language models for personalized explainable recommendations in media
  Ulysse Maes (imec-SMIT, Vrije Universiteit Brussel), Annelien Smets (imec-SMIT, Vrije Universiteit Brussel) & Tim Raats (imec-SMIT, Vrije Universiteit Brussel)

• LLM4Jobs: Leveraging Large Language Models for Occupation Extraction and Standardization in Job Recommendation Systems
  Nan Li (Ghent University), Bo Kang (Ghent University) & Tijl De Bie (Ghent University)

• Multi-Armed-Bandits for News Recommendation
  Bart Goethals (University of Antwerp) & Noah Daniels (University of Antwerp)

• Newsroom Realities: An exploration of changing dynamics in news organizations in relation to recommender systems
  Hanne Vandenbroucke (imec-SMIT, Vrije Universiteit Brussel) & Annelien Smets (imec-SMIT, Vrije Universiteit Brussel)

• Nudged to learn: Exploring longitudinal nudging effects in a news aggregator
  Nicolas Mattis (Vrije Universiteit Amsterdam), Lucien Heitz (University of Zuerich), Philipp Masur (Vrije Universiteit Amsterdam), Judith  Moeller (University of Hamburg in cooperation with the Leibniz Institute for Media Research | Hans-Bredow-Institut (HBI)) & Wouter van Atteveldt (Vrije Universiteit Amsterdam)

• Promoting Household Energy Conservation through Goal Setting and Signposting in a Rasch-Based Recommender System
  Aleid Oonk (Eindhoven University of Technology), Alain Starke (University of Amsterdam) & Martijn Willemsen (Eindhoven University of Technology & JADS)

• The Role of Message Framing and Consumption Motivation in Building Consumers’ Trust in the Recommender System Output  
  Yilan Wang (University of Amsterdam), Zeph van Berlo (University of Amsterdam) & Ivana Busljeta Banks (University of Amsterdam)

• User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study
  Julien Albert (University of Namur), Martin Balfroid (University of Namur), Miriam Doh (ULB), Jeremie Bogaert (UCLouvain), Luca La Fisca (UMons), Liesbet De Vos (University of Namur), Bryan Renard (University of Namur), Vincent Stragier (UMons) & Emmanuel Jean (Multitel)

• What will we be streaming tonight? And why?
  Isabelle Puskas (imec-SMIT, Vrije Universiteit Brussel), Noëmie Forest (imec-SMIT, Vrije Universiteit Brussel), Wendy Van den Broeck (imec-SMIT, Vrije Universiteit Brussel) & Tim Raats (imec-SMIT, Vrije Universiteit Brussel)