68 citations · 84 across the 10 of their papers we have counts for
27 papers
Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing
Peter Müllner, Stefan Schmerda, Dieter Theiler +2
Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data pr…
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
Emanuel Lacic, Dominik Kowald
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Spe…
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Dominik Kowald, Emanuel Lacic
Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative…
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…
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…