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.


Publications

Peer-Reviewed Journal and Conference Papers

ConGater_EACL_2024 screenshot

Shahed Masoudian, Volaucnik Cornelia, Markus Schedl, Navid Rekab-saz
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
The 18th Conference of the European Chapter of the Association for Computational Linguistics March 17-22, 2024, 2024

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

TIST_ReuseKnn screenshot

Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald
ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations
ACM Transactions on Intelligent Systems and Technology, 2023

CompVsUserPercep screenshot

Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Red_Words_RecSysHR screenshot

Deepak Kumar, Tessa Grosz, Elisabeth Greif, Navid Rekab-saz, Markus Schedl
Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning
Proceedings of the 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023) co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), 2023

MultiObjectiveHyperOpt screenshot

Marta Moscati, Yashar Deldjoo, Giulio Davide Carparelli, Markus Schedl
Multiobjective Hyperparameter Optimization of Recommender Systems
Proceedings of the 3rd Workshop on Perspectives on the Evaluation of Recommender Systems co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore., 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

DiffPrivacy screenshot

Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald
Differential Privacy in Collaborative Filtering Recommender Systems: A Review
Frontiers in Big Data - Recommender Systems, 2023

ControlledDA_EUSIPCO_2023 screenshot

Shahed Masoudian, Koutini Khaled, Markus Schedl, Widmer Gerhard, Navid Rekab-saz
Domain Information Control at Inference Time for Acoustic Scene Classification
31st European Signal Processing Conference, (EUSIPCO) 2023, Helsinki,Finland, September 4-8, 2023, 2023

AccMiscPop screenshot

Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Advances in Bias and Fairness in Information Retrieval, 2023

DAM_EACL_2023 screenshot

Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickoff, Markus Schedl, Navid Rekab-saz
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

BTC_Price_Sentiment screenshot

Markus Frohmann, Manuel Karner, Said Khudoyan, Robert Wagner, Markus Schedl
Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting
Big Data and Cognitive Computing, 2023

actr_cf_music screenshot

Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl
Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
Proceedings of the 17th ACM Conference on Recommender Systems (RecSys), 2023

ModularDebiasing_ACLFindings_2023 screenshot

Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekab-saz
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Findings of the Association for Computational Linguistics: ACL 2023, 2023

VocalTrainML_CMMR_2023 screenshot

Antonia Stadler, Emilia Parada-Cabaleiro, Markus Schedl
Towards Potential Applications of Machine Learning in Computer-Assisted Vocal Training
Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research (CMMR 2023), ., 2023

MusEmo_CMMR_2023 screenshot

Emilia Parada-Cabaleiro, Anton Batliner, Maximilian Schmitt, Björn Schuller, Markus Schedl
Music Emotions in Solo Piano: Bridging the Gap Between Human Perception and Machine Learning
Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research (CMMR 2023), ., 2023

Tutorial_Recsys_2023 screenshot

Markus Schedl, Vito Walter Anelli, Elisabeth Lex
Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives
Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Exploring_LDK_2023 screenshot

Raisa Romanov Geleta, Klaus Eckelt, Emilia Parada-Cabaleiro, Markus Schedl
Exploring Intensities of Hate Speech on Social Media: A Case Study on Explaining Multilingual Models with XAI
Proceedings of the 4th Conference on Language, Data and Knowledge, 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

CountryBias screenshot

Oleg Lesota, Emilia Parada-Cabaleiro, Stefan Brandl, Elisabeth Lex, Navid Rekab-saz, Markus Schedl
Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms
Proceedings of the 23rd International Society for Music Information Retrieval Conference, ISMIR 2022, Bengaluru, India, December 4-8, 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

Peter Knees, Markus Schedl, Bruce Ferwerda, Audrey Laplante
Editors: Mirjam Augstein, Eelco Herder, Wolfgang Wörndl
Chapter: Listener awareness in music recommender systems: directions and current trends
Personalized Human-Computer Interaction
De Gruyter Oldenbourg, 2023.

Markus Schedl, Elisabeth Lex
Editors: Mirjam Augstein, Eelco Herder, Wolfgang Wörndl
Chapter: Fairness of information access system
Personalized Human-Computer Interaction
De Gruyter Oldenbourg, 2023.

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.