Schedl, Markus

Personality Bias of Music Recommendation Algorithms

Melchiorre, A. B., Zangerle, E., & Schedl, M. (2020) · Fourteenth ACM Conference on Recommender Systems

Recommender Systems and TechniquesEthics and Social Impacts of AI

Cited by 48

Unlearning Protected User Attributes in Recommendations with Adversarial Training

Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., & Schedl, M. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesEthics and Social Impacts of AI

Cited by 47

The relation of culture, socio-economics, and friendship to music preferences: A large-scale, cross-country study

Liu, M., Hu, X., & Schedl, M. (2018) · PLOS ONE

Recommender Systems and TechniquesMusic History and Culture

Cited by 43

Grep-BiasIR: A dataset for investigating gender representation bias in information retrieval results

Krieg, K., Parada-Cabaleiro, E., Medicus, G., Lesota, O., Schedl, M., & Rekabsaz, N. (2023) · Proceedings of the 2023 Conference on Human Information Interaction and Retrieval

Ethics and Social Impacts of AIInformation Retrieval and Search Behavior

Cited by 31

ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations

Müllner, P., Lex, E., Schedl, M., & Kowald, D. (2023) · ACM Transactions on Intelligent Systems and Technology

Privacy-Preserving Technologies in DataRecommender Systems and Techniques

Cited by 27