68 citations · 120 across the 15 of their papers we have counts for
23 papers · 1 filter
Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study
Oleg Lesota, Gustavo Escobedo, Bruce Ferwerda +4
Popularity calibration in recommender systems has been studied both as a form of user-centered personalization and as an indicator of popularity bias. Most existing work evaluates…
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…
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
Tomislav Duricic, Dominik Kowald, Emanuel Lacic +1
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches…
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Dominik Kowald, Gregor Mayr, Markus Schedl +1
Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, a…
Position Paper on Simulating Privacy Dynamics in Recommender Systems
Peter Müllner, Elisabeth Lex, Dominik Kowald
In this position paper, we discuss the merits of simulating privacy dynamics in recommender systems. We study this issue at hand from two perspectives: Firstly, we present a concep…
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz +4
Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety o…