Christian Ganhör

Christian Ganhör

During my studies at Johannes Kepler University Linz, I worked as a student research assistant at the Institute of Computational Perception. My research focused on Recommender Systems - initially on mitigating inherent biases, and later on leveraging multimodal side information to address cold-start and missing-modality scenarios. My work has been published in renowed conferences and journals, including SIGIR, RecSys, and TORS.

In addition, I contributed to EmoMTB, a science communication project presented at the Ars Electronica Festival, with which we showcased an interactive music exploration interface.

For the latest updates and my contact details, please visit my website.


Publications

Peer-Reviewed Journal and Conference Papers

advx-multvae screenshot

Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, Markus Schedl
Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
Proceedings of the 5th International Workshop on Algorithmic Bias in Search and Recommendation (BIAS @ SIGIR 2024), 2024

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Christian Ganhör, David Penz, Navid Rekab-saz, Oleg Lesota, Markus Schedl
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022

EMOMTB screenshot

Alessandro B. Melchiorre, David Penz, Christian Ganhör, Oleg Lesota, Vasco Bezold Rosner Fragoso, Florian Friztl, Emilia Parada-Cabaleiro, Franz Schubert, Markus Schedl
EmoMTB: Emotion-aware Music Tower Blocks
Proceedings of the 2022 ACM International Conference on Multimedia Retrieval (ICMR), 2022


Theses