Publications

2024

1. 

   Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models

   Gustavo Escobedo, Marta Moscati, Peter Muellner, Simone Kopeinik, Dominik Kowald, Elisabeth Lex, and Markus Schedl

   In Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VII, 2024

   Bib HTML PDF

   @inproceedings{DBLP:conf/pkdd/EscobedoMMKKLS24,
     author = {Escobedo, Gustavo and Moscati, Marta and Muellner, Peter and Kopeinik, Simone and Kowald, Dominik and Lex, Elisabeth and Schedl, Markus},
     editor = {Bifet, Albert and Davis, Jesse and Krilavicius, Tomas and Kull, Meelis and Ntoutsi, Eirini and Zliobaite, Indre},
     title = {Making Alice Appear Like Bob: {A} Probabilistic Preference Obfuscation
                       Method For Implicit Feedback Recommendation Models},
     booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track
                       - European Conference, {ECML} {PKDD} 2024, Vilnius, Lithuania, September
                       9-13, 2024, Proceedings, Part {VII}},
     series = {Lecture Notes in Computer Science},
     volume = {14947},
     pages = {349--365},
     publisher = {Springer},
     year = {2024},
     url = {https://doi.org/10.1007/978-3-031-70368-3\_21},
     doi = {10.1007/978-3-031-70368-3\_21},
   }

2. 

   Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training

   Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, and Markus Schedl

   In Proceedings of the 5th International Workshop on Algorithmic Bias in Search and Recommendation BIAS @ SIGIR (2024), 2024

   Bib PDF

   @inproceedings{DBLP:conf/sigir/EscobedoRSDLL24,
     author = {Escobedo, Gustavo and Ganh{\"{o}}r, Christian and Brandl, Stefan and Augstein, Mirjam and Schedl, Markus},
     title = {Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training},
     booktitle = {Proceedings of the 5th International Workshop on Algorithmic Bias in Search and Recommendation BIAS @ SIGIR (2024)},
     year = {2024},
   }

2023

1. Computational Versus Perceived Popularity Miscalibration in Recommender Systems

   Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, and Markus Schedl

   In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, 2023

   Bib HTML

   @inproceedings{DBLP:conf/sigir/LesotaEDFKLRS23,
     author = {Lesota, Oleg and Escobedo, Gustavo and Deldjoo, Yashar and Ferwerda, Bruce and Kopeinik, Simone and Lex, Elisabeth and Rekabsaz, Navid and Schedl, Markus},
     editor = {Chen, Hsin{-}Hsi and Duh, Wei{-}Jou (Edward) and Huang, Hen{-}Hsen and Kato, Makoto P. and Mothe, Josiane and Poblete, Barbara},
     title = {Computational Versus Perceived Popularity Miscalibration in Recommender
                       Systems},
     booktitle = {Proceedings of the 46th International {ACM} {SIGIR} Conference on
                       Research and Development in Information Retrieval, {SIGIR} 2023, Taipei,
                       Taiwan, July 23-27, 2023},
     pages = {1889--1893},
     publisher = {{ACM}},
     year = {2023},
     url = {https://doi.org/10.1145/3539618.3591964},
     doi = {10.1145/3539618.3591964},
   }