output
19982026
most citedLearning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle

200 citations

90 papers

cs.DL2026

What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives

Florian Atzenhofer-Baumgartner, Dominik Kowald

User models for recommender systems (RecSys) typically assume stable preferences, similarity-based relevance, and session-bounded interactions -- assumptions derived from high-volu…

cs.HC2026

Shifting Engagement With Cybersecurity: How People Discover and Share Cybersecurity Content at Work and at Home

William Seymour, Martin J. Kraemer

Cybersecurity awareness is shaped by a wide range of professional and personal experiences, including information and training at work and the sharing of news and other content at…

cs.HC2025★ 1 cited

A Study of Performance and Interaction Patterns in Hand and Tangible Interaction in Tabletop Mixed Reality

Carlos Mosquera, Neven Elsayed, Ernst Kruijff +2

This paper presents a comprehensive study of virtual 3D object manipulation along 4DoF on real surfaces in mixed reality (MR), using hand-based and tangible interactions. A custom…

cs.IR2025★ 11 cited

Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?

Peter Muellner, Anna Schreuer, Simone Kopeinik +2

Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms of…

cs.IR2025★ 1 cited

A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives

Florian Atzenhofer-Baumgartner, Georg Vogeler, Dominik Kowald

This paper presents the first multistakeholder approach for translating diverse stakeholder values into an evaluation metric setup for Recommender Systems (RecSys) in digital archi…

cs.IR2025★ 1 cited

Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations

Andrea Forster, Simone Kopeinik, Denic Helic +2

Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popula…