68 citations · 336 across the 43 of their papers we have counts for
43 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…
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
Peter Müllner, Elisabeth Lex, Markus Schedl +1
Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to…
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
Tomislav Duricic, Dominik Kowald, Emanuel Lacic +1
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches…
mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection
Markus Reiter-Haas, Alexander Ertl, Kevin Innerebner +1
This paper presents the winning system for the zero-shot Spanish framing detection task, which also achieves competitive places in eight additional languages. The challenge of the…
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…
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
Hussain Hussain, Meng Cao, Sandipan Sikdar +4
We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a partic…
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…
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…
Position Paper on Simulating Privacy Dynamics in Recommender Systems
Peter Müllner, Elisabeth Lex, Dominik Kowald
In this position paper, we discuss the merits of simulating privacy dynamics in recommender systems. We study this issue at hand from two perspectives: Firstly, we present a concep…
Cross-platform analysis of user comments in YouTube videos linked on Reddit conspiracy theory forum
Tomislav Duricic, Volker Seiser, Elisabeth Lex
We perform a cross-platform analysis in which we study how does linking YouTube content on Reddit conspiracy forum impact language used in user comments on YouTube. Our findings sh…
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…
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +3
Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial attacks on graph data. Common attack approaches are typically informed, i.e. they have access…
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…
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…
Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
Peter Müllner, Dominik Kowald, Elisabeth Lex
In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction…
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +2
Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. I…
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…
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
Tomislav Duricic, Hussain Hussain, Emanuel Lacic +3
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly a…
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…
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…
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
Dominik Kowald, Matthias Traub, Dieter Theiler +5
This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relations…
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric
Emanuel Lacic, Dominik Kowald, Dieter Theiler +4
In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation sys…
The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
Elisabeth Lex, Dominik Kowald
In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model h…