313 citations · 852 across the 32 of their papers we have counts for
32 papers
How Far Can Pretrained LLMs Go in Symbolic Music? Controlled Comparisons of Supervised and Preference-based Adaptation
Deepak Kumar, Emmanouil Karystinaios, Gerhard Widmer +1
Music often shares notable parallels with language, motivating the use of pretrained large language models (LLMs) for symbolic music understanding and generation. Despite growing i…
Domain Information Control at Inference Time for Acoustic Scene Classification
Shahed Masoudian, Khaled Koutini, Markus Schedl +2
Domain shift is considered a challenge in machine learning as it causes significant degradation of model performance. In the Acoustic Scene Classification task (ASC), domain shift…
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Dominik Kowald, Gregor Mayr, Markus Schedl +1
Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, a…
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas +4
Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial train…
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Christian Ganhör, David Penz, Navid Rekabsaz +2
Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, r…
ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations
Peter Müllner, Elisabeth Lex, Markus Schedl +1
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk…
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar +2
Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks. Common in-processing bias mitigation approaches, such as adver…
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?
Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl +1
This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators. In particular, since relevance…
Explainability in Music Recommender Systems
Darius Afchar, Alessandro B. Melchiorre, Markus Schedl +3
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing thes…
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus +3
The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establish…
My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music
Tomislav Duricic, Dominik Kowald, Markus Schedl +1
Homophily describes the phenomenon that similarity breeds connection, i.e., individuals tend to form ties with other people who are similar to themselves in some aspect(s). The sim…
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz +4
Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety o…
Predicting Music Relistening Behavior Using the ACT-R Framework
Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl +3
Providing suitable recommendations is of vital importance to improve the user satisfaction of music recommender systems. Here, users often listen to the same track repeatedly and a…
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges
Yashar Deldjoo, Markus Schedl, Peter Knees
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collabo…
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Oleg Lesota, Navid Rekabsaz, Daniel Cohen +3
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich litera…
Current Challenges and Future Directions in Podcast Information Access
Rosie Jones, Hamed Zamani, Markus Schedl +11
Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and c…
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers
Navid Rekabsaz, Simone Kopeinik, Markus Schedl
Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established…
TripClick: The Log Files of a Large Health Web Search Engine
Navid Rekabsaz, Oleg Lesota, Markus Schedl +2
Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularl…
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
Dominik Kowald, Peter Muellner, Eva Zangerle +3
Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by…
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
Markus Schedl, Christine Bauer, Wolfgang Reisinger +2
Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listeni…
Do Neural Ranking Models Intensify Gender Bias?
Navid Rekabsaz, Markus Schedl
Concerns regarding the footprint of societal biases in information retrieval (IR) systems have been raised in several previous studies. In this work, we examine various recent IR m…
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
Dominik Kowald, Elisabeth Lex, Markus Schedl
In this paper, we introduce a psychology-inspired approach to model and predict the music genre preferences of different groups of users by utilizing human memory processes. These…
Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young
Markus Schedl, Christine Bauer
In this paper, we analyze a large dataset of user-generated music listening events from Last.fm, focusing on users aged 6 to 18 years. Our contribution is two-fold. First, we study…
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
Dominik Kowald, Markus Schedl, Elisabeth Lex
Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work…