Gustavo Escobedo

I am a PhD student at the Institute for Computational Perception at Johannes Kepler University Linz, Austria and researcher in the project Human-Centered AI under the supervision of Prof. Dr. Markus Schedl. My main research interests are on Debiasing and Fairness for Recommender Systems. Previously, I had applied Reinforcement Learning algorithms to introduce personalization into Session-based Recommender Systems. Also, I hold meaningful work experience in Data Science and Software Development.

Selected Publications

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},
}

Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based Regularization

Gustavo Escobedo, David Penz, and Markus Schedl

In Proceedings of the Nineteenth ACM Conference on Recommender Systems, 2025

Bib

@inproceedings{escobedo2025debiasing,
  title = {Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based Regularization},
  author = {Escobedo, Gustavo and Penz, David and Schedl, Markus},
  booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},
  pages = {1153--1158},
  year = {2025},
}

Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation

David Penz, Gustavo Escobedo, and Markus Schedl

In Proceedings of the Nineteenth ACM Conference on Recommender Systems, 2025

Bib

@inproceedings{penz2025mitigating,
  title = {Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation},
  author = {Penz, David and Escobedo, Gustavo and Schedl, Markus},
  booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},
  pages = {632--636},
  year = {2025},
}