activity
20152021
most citedAnalyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

68 citations · 118 across the 12 of their papers we have counts for

collaborators

28 papers

cs.SI20219 cited

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…

cs.IR2021

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…

cs.SI20211 cited

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…

cs.IR202168 cited

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…

cs.LG202116 cited

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…

cs.IR2021

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…