Markus Schedl

Markus Schedl

I am a Full Professor at Johannes Kepler University (JKU) Linz, Austria, affiliated with the Institute of Computational Perception, where I lead the Multimedia Mining and Search (MMS) group. In addition, I am head of the Human-centered Artificial Intelligence (HCAI) group at the Linz Institute of Technology (LIT) AI Lab. My areas of expertise include recommender systems, user modeling, information retrieval, machine learning, natural language processing, multimedia, data analysis, and web mining.

I graduated in Computer Science from the Vienna University of Technology (TU Wien) and earned my PhD degree from JKU Linz. In addition, I studied International Business Administration at the Vienna University of Economics and Business (WU Wien) and the University of Gothenburg (School of Business, Economics and Law), which led to a Master’s degree.

I have been leading and co-leading projects funded by the Austrian Science Fund (FWF), the Austrian Research Promotion Agency (FFG), and the European Commission (EC). I also maintain collaborations with industry, for instance with Siemens, Spotify, and Deezer. Furthermore, I serve as consultant on the topics mentioned above.

I am also a passionate teacher and regularly give courses at JKU (Introduction to Machine Learning, Multimedia Search and Retrieval, Learning from User-generated Data, Multimedia Data Mining, and Social Media Mining and Analysis). In addition, I spent several guest lecturing stays among others at Universitat Pompeu Fabra Barcelona, Queen Mary University of London, and Kungliga Tekniska Högskolan Stockholm.


Peer-Reviewed Journal and Conference Papers

Tutorial_WSDM_2023 screenshot

Markus Schedl, Emilia Gómez, Elisabeth Lex
Trustworthy Algorithmic Ranking Systems
Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM 2023), 2023

Demo_WSDM_2023 screenshot

Veronika Arefieva, Roman Egger, Michael Schrefl, Markus Schedl
Travel Bird: A Personalized Destination Recommender with TourBERT and Airbnb Experiences
Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM 2023), 2023

HumanVSMachine screenshot

Emilia Parada-Cabaleiro, Anton Batliner, Maximilian Schmitt, Markus Schedl, Giovanni Costantini, Björn Schuller
Perception and classification of emotions in nonsense speech: Humans versus machines
PLOS ONE, 2023

UnlearningAdversarial screenshot

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

EuroReg screenshot

Tommaso Di Noia, Nava Tintarev, Panagiota Fatourou, Markus Schedl
Recommender Systems under European AI Regulations
Communications of the ACM, 2022

Music4AllOnion screenshot

Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle, Markus Schedl
Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM), 2022

MusicAnxietyAcoustic screenshot

Emilia Parada-Cabaleiro, Anton Batliner, Markus Schedl
An Exploratory Study on the Acoustic Musical Properties to Decrease Self-Perceived Anxiety
International Journal of Environmental Research and Public Health, 2022

CaliPopExploring screenshot

Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro B. Melchiorre, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th {ACM} Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September 2022, 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

GCNext screenshot

Andreas Peintner, Marta Moscati, Emilia Parada-Cabaleiro, Markus Schedl, Eva Zangerle
Unsupervised Graph Embeddings for Session-based Recommendation with Item Features
CARS Workshop on Context-Aware Recommender Systems (RecSys), 2022

ExpMusic screenshot

Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, Manuel Moussallam
Explainability in music recommender systems
AI Magazine, 2022

AffectMult screenshot

Mihai Gabriel Constantin, Liviu-Daniel Ştefan, Bogdan Ionescu, Claire-Hélène Demarty, Mats Sjöberg, Markus Schedl, Guillaume Gravier
Affect in Multimedia: Benchmarking Violent Scenes Detection
IEEE Transactions on Affective Computing, 2022

TextureRecognition screenshot

Emilia Parada-Cabaleiro, Maximilian Schmitt, Anton Batliner, Björn Schuller, Markus Schedl
Automatic Recognition of Texture in Renaissance Music
International Society for Music Information Retrieval, 2021

SupportUnderground screenshot

Dominik Kowald, Peter Muellner, Eva Zangerle, Christine Bauer, Markus Schedl, Elisabeth Lex
Support the underground: characteristics of beyond-mainstream music listeners
EPJ Data Science, 2021

PopBiasGender screenshot

Oleg Lesota, Alessandro B. Melchiorre, Navid Rekab-saz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021

PsychInformed screenshot

Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexander Felfernig, Markus Schedl
Psychology-Informed Recommender Systems
Foundations and Trends in Information Retrieval, 2021

ListenerModeling screenshot

Markus Schedl, Christine Bauer, Wolfgang Reisinger, Dominik Kowald, Elisabeth Lex
Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes
Frontiers in Artificial Intelligence, 2021

LEMONS screenshot

Alessandro B. Melchiorre, Verena Haunschmid, Markus Schedl, Gerhard Widmer
LEMONS: Listenable Explanations for Music recOmmeNder Systems
Proceedings of the 43rd European Conference on Information Retrieval (ECIR), 2021

DeepGenIR screenshot

Oleg Lesota, Navid Rekab-saz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021

UserModels screenshot

Eva Zangerle, Martin Pichl, Markus Schedl
User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues
Transactions of the International Society for Music Information Retrieval, 2020

RSLeveraging screenshot

Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi
Recommender Systems Leveraging Multimedia Content
ACM Computing Surveys, 2020

PersonalityCorrelates screenshot

Alessandro B. Melchiorre, Markus Schedl
Personality Correlates of Music Audio Preferences for Modelling Music Listeners
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP), 2020

PersonalityBias screenshot

Alessandro B. Melchiorre, Eva Zangerle, Markus Schedl
Personality Bias of Music Recommendation Algorithms
Proceedings of the 14th ACM Conference on Recommender Systems (RecSys), 2020

ModelingPopularity screenshot

Elisabeth Lex, Dominik Kowald, Markus Schedl
Modeling Popularity and Temporal Drift of Music Genre Preferences
Transactions of the International Society for Music Information Retrieval, 2020

ModelingPeresonalized screenshot

Marko Tkalčič, Markus Schedl, Peter Knees
Preface to the Special Issue on User Modeling for Personalized Interaction with Music
User Modeling and User-Adapted Interaction, 2020

IntUserInterfaces screenshot

Peter Knees, Markus Schedl, Masataka Goto
Intelligent User Interfaces for Music Discovery
Transactions of the International Society for Music Information Retrieval, 2020

Book Chapters

Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov
Editors: Francesco Ricci, Lior Rokach, Bracha Shapira
Chapter: Music Recommendation Systems: Techniques, Use Cases, and Challenges
Recommender Systems Handbook (3rd edition)
Springer, 2022.

Yashar Deldjoo, Markus Schedl, Balazs Hidasi, Yinwei Wei, Xiangnan He
Editors: Francesco Ricci, Lior Rokach, Bracha Shapira
Chapter: Multimedia Recommender Systems: Algorithms and Challenges
Recommender Systems Handbook (3rd edition)
Springer, 2022.