11 citations · 18 across the 7 of their papers we have counts for
7 papers
Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD
Vedad Misirlic, Gregor Mayr, Elisabeth Lex
Short-form video platforms use recommender systems to maximize engagement through highly efficient personalized recommendations. However, the impact of these recommendations on use…
Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
Peter Müllner, Dominik Kowald, Markus Schedl +1
Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades accuracy. We address this tra…
Graph Queries from Natural Language using Constrained Language Models and Visual Editing
Benedikt Kantz, Kevin Innerebner, Peter Waldert +3
Querying knowledge bases using ontologies is usually performed using dedicated query languages, question-answering systems, or visual query editors for Knowledge Graphs. We propose…
Differentiable Fuzzy Neural Networks for Recommender Systems
Stephan Bartl, Kevin Innerebner, Elisabeth Lex
As recommender systems become increasingly complex, transparency is essential to increase user trust, accountability, and regulatory compliance. Neuro-symbolic approaches that inte…
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
Kevin Innerebner, Dominik Kowald, Markus Schedl +1
Recommender systems often rely on sub-symbolic machine learning approaches that operate as opaque black boxes. These approaches typically fail to account for the cognitive processe…
OnSET: Ontology and Semantic Exploration Toolkit
Benedikt Kantz, Kevin Innerebner, Peter Waldert +3
Retrieval over knowledge graphs is usually performed using dedicated, complex query languages like SPARQL. We propose a novel system, Ontology and Semantic Exploration Toolkit (OnS…
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
Gustavo Escobedo, Marta Moscati, Peter Muellner +4
Users' interaction or preference data used in recommender systems carry the risk of unintentionally revealing users' private attributes (e.g., gender or race). This risk becomes pa…