71 citations · 304 across the 19 of their papers we have counts for
4 papers · 2 filters
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…
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…
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…