313 citations · 852 across the 32 of their papers we have counts for
6 papers · 1 filter
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Christian Ganhör, David Penz, Navid Rekabsaz +2
Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, r…
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…
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar +2
Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks. Common in-processing bias mitigation approaches, such as adver…
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?
Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl +1
This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators. In particular, since relevance…
Explainability in Music Recommender Systems
Darius Afchar, Alessandro B. Melchiorre, Markus Schedl +3
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing thes…
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus +3
The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establish…