1 code implementation • 8 Jan 2024 • Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald
In this work, we study how DP impacts recommendation accuracy and popularity bias, when applied to the training data of state-of-the-art recommendation models.
1 code implementation • 18 Oct 2023 • Armin Haberl, Jürgen Fleiß, Dominik Kowald, Stefan Thalmann
If an entry-level graphics card is available, the transcription speed increases to 20% of the audio duration.
no code implementations • 3 Oct 2023 • Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex
This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective.
no code implementations • 19 Jul 2023 • Harald Semmelrock, Simone Kopeinik, Dieter Theiler, Tony Ross-Hellauer, Dominik Kowald
Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce.
3 code implementations • 1 Mar 2023 • Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex
However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing.
no code implementations • 7 Feb 2023 • Sebastian Scher, Bernhard Geiger, Simone Kopeinik, Andreas Trügler, Dominik Kowald
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings.
1 code implementation • 3 Jan 2023 • Emanuel Lacic, Tomislav Duricic, Leon Fadljevic, Dieter Theiler, Dominik Kowald
In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements.
no code implementations • 21 Oct 2022 • Peter Müllner, Stefan Schmerda, Dieter Theiler, Stefanie Lindstaedt, Dominik Kowald
We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios.
no code implementations • 17 Aug 2022 • Sebastian Scher, Simone Kopeinik, Andreas Trügler, Dominik Kowald
We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.
1 code implementation • 23 Jun 2022 • Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process.
no code implementations • 2 Mar 2022 • 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.
3 code implementations • 1 Mar 2022 • Dominik Kowald, Emanuel Lacic
In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists.
no code implementations • 29 Nov 2021 • Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck, Stefanie Lindstaedt, Dominik Kowald
In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation.
no code implementations • 14 Sep 2021 • Peter Müllner, Elisabeth Lex, Dominik Kowald
With this work, we hope to present perspectives on how privacy-aware simulations could be realized, such that they enable researchers to study the dynamics of privacy within a recommender system.
no code implementations • 16 Aug 2021 • Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl
In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's tau rank-order correlation).
1 code implementation • 24 Feb 2021 • Dominik Kowald, Peter Muellner, Eva Zangerle, Christine Bauer, Markus Schedl, Elisabeth Lex
In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided.
1 code implementation • 18 Jan 2021 • 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 to preserve users' privacy.
no code implementations • 11 Sep 2020 • Markus Schedl, Christine Bauer, Wolfgang Reisinger, Dominik Kowald, Elisabeth Lex
To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences.
no code implementations • 30 Mar 2020 • Tomislav Duricic, Hussain Hussain, Emanuel Lacic, Dominik Kowald, Denis Helic, Elisabeth Lex
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering.
no code implementations • 24 Mar 2020 • 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.
3 code implementations • 10 Dec 2019 • Dominik Kowald, Markus Schedl, Elisabeth Lex
The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items.
no code implementations • 12 Aug 2019 • Dominik Kowald, Matthias Traub, Dieter Theiler, Heimo Gursch, Emanuel Lacic, Stefanie Lindstaedt, Roman Kern, Elisabeth Lex
The presented work contributes to the tripartite recommendation problem in general and to the under-researched portfolio of evaluating recommender systems for data markets in particular.
no code implementations • 12 Aug 2019 • Emanuel Lacic, Dominik Kowald, Dieter Theiler, Matthias Traub, Lucky Kuffer, Stefanie Lindstaedt, Elisabeth Lex
Our idea is to mimic the vocabulary of users in Amazon, who search for and review e-books, and to combine these search terms with editor tags in a hybrid tag recommendation approach.
1 code implementation • 2 Aug 2019 • Elisabeth Lex, Dominik Kowald
Here [KPL17], we adopt the BLL equation to model temporal reuse patterns of individual (i. e., reusing own hashtags) and social hashtags (i. e., reusing hashtags, which has been previously used by a followee) and to build a cognitive-inspired hashtag recommendation algorithm.
no code implementations • 23 Jul 2019 • Dominik Kowald, Elisabeth Lex, Markus Schedl
Music recommender systems have become central parts of popular streaming platforms such as Last. fm, Pandora, or Spotify to help users find music that fits their preferences.
no code implementations • 12 Jun 2019 • Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex
Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust.
no code implementations • 18 Jul 2018 • Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex
In our work, we explore the use of a measure from network science, i. e. regular equivalence, applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for recommending items.
1 code implementation • 30 Jan 2015 • Paul Seitlinger, Dominik Kowald, Simone Kopeinik, Ilire Hasani-Mavriqi, Tobias Ley, Elisabeth Lex
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation.