Markus Schedl

Markus Schedl

Johannes Kepler Universität Linz

H-index: 52

Europe-Austria

About Markus Schedl

Markus Schedl, With an exceptional h-index of 52 and a recent h-index of 38 (since 2020), a distinguished researcher at Johannes Kepler Universität Linz, specializes in the field of Recommender Systems, Information Retrieval, Multimedia, Human-centered AI, Trustworthy AI.

His recent articles reflect a diverse array of research interests and contributions to the field:

Psychology-informed Information Access Systems Workshop

Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters

Song lyrics have become simpler and more repetitive over the last five decades

Transparent Music Preference Modeling and Recommendation with a Model of Human Memory Theory

Measuring Bias in Search Results Through Retrieval List Comparison

Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan

The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias

Introduction to the ICWE 2022 Special Issue

Markus Schedl Information

University

Position

Professor at Institute of Computational Perception

Citations(all)

9947

Citations(since 2020)

5479

Cited By

6310

hIndex(all)

52

hIndex(since 2020)

38

i10Index(all)

192

i10Index(since 2020)

131

Email

University Profile Page

Johannes Kepler Universität Linz

Google Scholar

View Google Scholar Profile

Markus Schedl Skills & Research Interests

Recommender Systems

Information Retrieval

Multimedia

Human-centered AI

Trustworthy AI

Top articles of Markus Schedl

Title

Journal

Author(s)

Publication Date

Psychology-informed Information Access Systems Workshop

Markus Schedl

Marta Moscati

Bruno Sguerra

Romain Hennequin

Elisabeth Lex

2024/3/4

Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters

arXiv preprint arXiv:2401.16457

Shahed Masoudian

Cornelia Volaucnik

Markus Schedl

2024/1/29

Song lyrics have become simpler and more repetitive over the last five decades

Scientific Reports

Emilia Parada-Cabaleiro

Maximilian Mayerl

Stefan Brandl

Marcin Skowron

Markus Schedl

...

2024/3/28

Transparent Music Preference Modeling and Recommendation with a Model of Human Memory Theory

Dominik Kowald

Markus Reiter-Haas

Simone Kopeinik

Markus Schedl

Elisabeth Lex

2024/5/1

Measuring Bias in Search Results Through Retrieval List Comparison

Linda Ratz

Markus Schedl

Simone Kopeinik

Navid Rekabsaz

2024/3/23

Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan

arXiv preprint arXiv:2404.09342

Muhammad Saad Saeed

Shah Nawaz

Muhammad Salman Tahir

Rohan Kumar Das

Muhammad Zaigham Zaheer

...

2024/4/14

The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias

Peter Müllner

Elisabeth Lex

Markus Schedl

Dominik Kowald

2024/3/15

Introduction to the ICWE 2022 Special Issue

Journal of Web Engineering

Tommaso Di Noia

In-Young Ko

Markus Schedl

2023/1

Perception and classification of emotions in nonsense speech: Humans versus machines

Plos one

Emilia Parada-Cabaleiro

Anton Batliner

Maximilian Schmitt

Markus Schedl

Giovanni Costantini

...

2023/1/30

I don’t care how popular you are! investigating popularity bias in music recommendations from a user’s perspective

Bruce Ferwerda

Eveline Ingesson

Michaela Berndl

Markus Schedl

2023/3/19

Domain Information Control at Inference Time for Acoustic Scene Classification

Shahed Masoudian

Khaled Koutini

Markus Schedl

Gerhard Widmer

Navid Rekabsaz

2023/9/4

Predicting the price of Bitcoin using sentiment-enriched time series forecasting

Big Data and Cognitive Computing

Markus Frohmann

Manuel Karner

Said Khudoyan

Robert Wagner

Markus Schedl

2023/7/31

Exploring Intensities of Hate Speech on Social Media: A Case Study on Explaining Multilingual Models with XAI

Raisa Romanov Geleta

Klaus Eckelt

Emilia Parada-Cabaleiro

Markus Schedl

2023/9

Differential privacy in collaborative filtering recommender systems: a review

Peter Müllner

Elisabeth Lex

Markus Schedl

Dominik Kowald

2023

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

Klara Krieg

Emilia Parada-Cabaleiro

Gertraud Medicus

Oleg Lesota

Markus Schedl

...

2023/3/19

Computational Versus Perceived Popularity Miscalibration in Recommender Systems

Oleg Lesota

Gustavo Escobedo

Yashar Deldjoo

Bruce Ferwerda

Simone Kopeinik

...

2023/7/19

ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations

ACM Transactions on Intelligent Systems and Technology

Peter Müllner

Elisabeth Lex

Markus Schedl

Dominik Kowald

2023/8/12

Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives

Deepak Kumar

Tessa Grosz

Navid Rekabsaz

Elisabeth Greif

Markus Schedl

2023

Travel bird: A personalized destination recommender with tourbert and airbnb experiences

Veronika Arefieva

Roman Egger

Michael Schrefl

Markus Schedl

2023/2/27

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation

International Journal of Multimedia Information Retrieval

Alessandro B Melchiorre

David Penz

Christian Ganhör

Oleg Lesota

Vasco Fragoso

...

2023/6

See List of Professors in Markus Schedl University(Johannes Kepler Universität Linz)

Co-Authors

H-index: 63
Gerhard Widmer

Gerhard Widmer

Johannes Kepler Universität Linz

H-index: 45
Emilia Gómez

Emilia Gómez

Universidad Pompeu Fabra

H-index: 36
Paolo Cremonesi

Paolo Cremonesi

Politecnico di Milano

H-index: 34
Bogdan Ionescu

Bogdan Ionescu

Universitatea Politehnica din Bucuresti

H-index: 32
Hamed Zamani

Hamed Zamani

University of Massachusetts Amherst

H-index: 31
Peter Knees

Peter Knees

Technische Universität Wien

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