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HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response


Oct 10, 2022
Selim Fekih, Nicolò Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz


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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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Parameter Efficient Diff Pruning for Bias Mitigation


May 30, 2022
Lukas Hauzenberger, Navid Rekabsaz


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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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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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CODER: An efficient framework for improving retrieval through COntextualized Document Embedding Reranking


Dec 16, 2021
George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff


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WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models


Dec 13, 2021
Benjamin Minixhofer, Fabian Paischer, 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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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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Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers


May 11, 2021
Navid Rekabsaz, Simone Kopeinik, Markus Schedl

* Accepted at SIGIR 2021 

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