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ReuseKNN: Neighborhood Reuse for Privacy-Aware Recommendations


Jun 23, 2022
Peter Müllner, Markus Schedl, Elisabeth Lex, Dominik Kowald

* 27 pages, 8 figures, 7 tables, under review 

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Unlearning Protected User Attributes in Recommendations with Adversarial Training


Jun 09, 2022
Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl

* Accepted at SIGIR 2022 

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Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?


Mar 03, 2022
Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz

* Accepted at workshop on Algorithmic Bias in Search and Recommendation at ECIR 2022 

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Explainability in Music Recommender Systems


Jan 25, 2022
Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, Manuel Moussallam

* To appear in AI Magazine, Special Topic on Recommender Systems 2022 

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Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results


Jan 19, 2022
Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz


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Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?


Aug 16, 2021
Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl

* RecSys 2021 - LBR 

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Predicting Music Relistening Behavior Using the ACT-R Framework


Aug 05, 2021
Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex

* Accepted for publication in RecSys'21 late-breaking results 

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Content-based Music Recommendation: Evolution, State of the Art, and Challenges


Jul 25, 2021
Yashar Deldjoo, Markus Schedl, Peter Knees

* 35 pages, 2 figures 

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A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models


Jun 25, 2021
Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl

* ICTIR'21 

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