6 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems
Andrea Forster, Peter Müllner, Denis Helic +2
LLM agents are increasingly used for personalization due to their ability to communicate directly with users in natural language, integrate external knowledge bases, and negotiate…
Establishing and Evaluating Trustworthy AI: Overview and Research Challenges
Dominik Kowald, Sebastian Scher, Viktoria Pammer-Schindler +13
Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable out…
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
Peter Müllner, Elisabeth Lex, Markus Schedl +1
Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to…
ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations
Peter Müllner, Elisabeth Lex, Markus Schedl +1
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk…
Technical Design Study for the PANDA Time Projection Chamber
M. Ball, F. V. Böhmer, S. Dørheim +38
This document illustrates the technical layout and the expected performance of a Time Projection Chamber as the central tracking system of the PANDA experiment. The detector is bas…