54 citations · 117 across the 18 of their papers we have counts for
18 papers
Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study
Oleg Lesota, Gustavo Escobedo, Bruce Ferwerda +4
Popularity calibration in recommender systems has been studied both as a form of user-centered personalization and as an indicator of popularity bias. Most existing work evaluates…
From Skill Extraction to Multistakeholder Recommendation: A Two-Stage Framework for Bias Governance in Skills-Based Job Matching
Andrea Forster, Gregor Autischer, Dominik Kowald +1
AI-based labor-market systems or platforms can affect access to job opportunities prior to organizational candidate rankings or hiring decisions. Such applications warrant caution,…
Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs
Richard Hohensinner, Belgin Mutlu, Inti Gabriel Mendoza Estrada +3
Large language models (LLMs) are deployed at scale, yet their training data life cycle remains opaque. This survey synthesizes research from the past ten years on three tightly cou…
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
Lea Demelius, Dominik Kowald, Simone Kopeinik +2
Differential privacy (DP) is a prominent method for protecting information about individuals during data analysis. Training neural networks with differentially private stochastic g…
Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
Peter Muellner, Anna Schreuer, Simone Kopeinik +2
Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms of…
Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language
Kristin Gnadt, David Thulke, Simone Kopeinik +1
In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods…
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
Andrea Forster, Simone Kopeinik, Denic Helic +2
Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popula…
Establishing and Evaluating Trustworthy AI: Overview and Research Challenges
Dominik Kowald, Sebastian Scher, Viktoria Pammer-Schindler +13
Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable out…
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
Gustavo Escobedo, Marta Moscati, Peter Muellner +4
Users' interaction or preference data used in recommender systems carry the risk of unintentionally revealing users' private attributes (e.g., gender or race). This risk becomes pa…
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Harald Semmelrock, Tony Ross-Hellauer, Simone Kopeinik +4
Many research fields are currently reckoning with issues of poor levels of reproducibility. Some label it a "crisis", and research employing or building Machine Learning (ML) model…
Reproducibility in Machine Learning-Driven Research
Harald Semmelrock, Simone Kopeinik, Dieter Theiler +2
Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learn…
A conceptual model for leaving the data-centric approach in machine learning
Sebastian Scher, Bernhard Geiger, Simone Kopeinik +2
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings. This has recently been cha…
Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
Sebastian Scher, Simone Kopeinik, Andreas Trügler +1
The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as i…
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…
Studying Moral-based Differences in the Framing of Political Tweets
Markus Reiter-Haas, Simone Kopeinik, Elisabeth Lex
In this paper, we study the moral framing of political content on Twitter. Specifically, we examine differences in moral framing in two datasets: (i) tweets from US-based politicia…
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems
Dominik Kowald, Simone Kopeinik, Elisabeth Lex
Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems,…
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics
Paul Seitlinger, Dominik Kowald, Simone Kopeinik +3
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpr…
Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender
Dominik Kowald, Paul Seitlinger, Christoph Trattner +1
We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumpt…