Gustavo Escobedo
Scholar

Gustavo Escobedo

Google Scholar ID: NwgcyoIAAAAJ
Johannes Kepler University Linz
Recommender SystemsUser privacyBias MitigationSession-BasedUser-Centered AI
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Citations & Impact
All-time
Citations
21
 
H-index
3
 
i10-index
1
 
Publications
10
 
Co-authors
0
 
Contact
Emailgustavo.escobedo@jku.atGitHubOpen ↗LinkedInOpen ↗
Publications
4 items
Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study
2026
Cited
0
Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
2026
Cited
0
Investigating Gender Bias in LLM-Generated Stories via Psychological Stereotypes
2025
Cited
0
Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
2025
Cited
0
Resume
Academic Achievements
  • Publications:
  • - 'Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models', ECML PKDD 2024
  • - 'Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based Regularization', ACM Conference on Recommender Systems 2025
  • - 'Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation', ACM Conference on Recommender Systems 2025
Research Experience
  • Researcher in the project Human-Centered AI.
Education
  • PhD student at the Institute for Computational Perception at Johannes Kepler University Linz, Austria, under the supervision of Prof. Dr. Markus Schedl.
Background
  • Research interests include debiasing and fairness for recommender systems. Previously, applied reinforcement learning algorithms to introduce personalization into session-based recommender systems. Holds meaningful work experience in data science and software development.