A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. - Oleg Lesota; Navid Rekabsaz; Daniel Cohen; Klaus Antonius Grasserbauer; Carsten Eickhoff; Markus Schedl (ICTIR, 2021)

Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? - Oleg Lesota; Alessandro B. Melchiorre; Navid Rekabsaz; Stefan Brandl; Dominik Kowald; Elisabeth Lex; Markus Schedl (RecSys, 2021)

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. - Daniel Cohen; Bhaskar Mitra 001; Oleg Lesota; Navid Rekabsaz; Carsten Eickhoff (SIGIR, 2021)

TripClick: The Log Files of a Large Health Web Search Engine. - Navid Rekabsaz; Oleg Lesota; Markus Schedl; Jon Brassey; Carsten Eickhoff (SIGIR, 2021)

Investigating gender fairness of recommendation algorithms in the music domain. - Alessandro B. Melchiorre; Navid Rekabsaz; Emilia Parada-Cabaleiro; Stefan Brandl; Oleg Lesota; Markus Schedl (Inf. Process. Manag., 2021)

LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. - Markus Schedl; Stefan Brandl; Oleg Lesota; Emilia Parada-Cabaleiro; David Penz; Navid Rekabsaz (CHIIR, 2022)

Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms. - Oleg Lesota; Emilia Parada-Cabaleiro; Stefan Brandl; Elisabeth Lex; Navid Rekabsaz; Markus Schedl (ISMIR, 2022)

EmoMTB: Emotion-aware Music Tower Blocks. - Alessandro B. Melchiorre; David Penz; Christian Ganhör; Oleg Lesota; Vasco Fragoso; Florian Friztl; Emilia Parada-Cabaleiro; Franz Schubert; Markus Schedl (ICMR, 2022)

Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization. - Oleg Lesota; Stefan Brandl; Matthias Wenzel; Alessandro B. Melchiorre; Elisabeth Lex; Navid Rekabsaz; Markus Schedl (MORS@RecSys, 2022)

Unlearning Protected User Attributes in Recommendations with Adversarial Training. - Christian Ganhör; David Penz; Navid Rekabsaz; Oleg Lesota; Markus Schedl (SIGIR, 2022)

Grep-BiasIR: A Dataset for Investigating Gender Representation Bias in Information Retrieval Results. - Klara Krieg; Emilia Parada-Cabaleiro; Gertraud Medicus; Oleg Lesota; Markus Schedl; Navid Rekabsaz (CHIIR, 2023)

Parameter-efficient Modularised Bias Mitigation via AdapterFusion. - Deepak Kumar; Oleg Lesota; George Zerveas; Daniel Cohen; Carsten Eickhoff; Markus Schedl; Navid Rekabsaz (EACL, 2023)

Computational Versus Perceived Popularity Miscalibration in Recommender Systems. - Oleg Lesota; Gustavo Escobedo; Yashar Deldjoo; Bruce Ferwerda; Simone Kopeinik; Elisabeth Lex; Navid Rekabsaz; Markus Schedl (SIGIR, 2023)

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation. - Alessandro B. Melchiorre; David Penz; Christian Ganhör; Oleg Lesota; Vasco Fragoso; Florian Fritzl; Emilia Parada-Cabaleiro; Franz Schubert; Markus Schedl (Int. J. Multim. Inf. Retr., 2023)

The Importance of Cognitive Biases in the Recommendation Ecosystem: Evidence of Feature-Positive Effect, Ikea Effect, and Cultural Homophily. - Markus Schedl; Oleg Lesota; Shahed Masoudian (IntRS@RecSys, 2024)

Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. - Oleg Lesota; Jonas Geiger; Max Walder; Dominik Kowald; Markus Schedl (RecSys, 2024)

Spin-Wave Voices: Sonification of Nanoscale Spin Waves as an Engagement and Research Tool. - Santa Pile; Oleg Lesota; Silvan David Peter; Christina Humer; Martin Gasser (CoRR, 2024)

The Importance of Cognitive Biases in the Recommendation Ecosystem. - Markus Schedl; Oleg Lesota; Stefan Brandl; Mohammad Lotfi; Gustavo Junior Escobedo Ticona; Shahed Masoudian (CoRR, 2024)

Iguanodon: A Code-Breaking Game for Improving Visualization Construction Literacy. - Patrick Adelberger; Oleg Lesota; Klaus Eckelt; Markus Schedl; Marc Streit (IEEE Trans. Vis. Comput. Graph., 2025)