68 citations · 250 across the 51 of their papers we have counts for
44 papers · 1 filter
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…
Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
Peter Müllner, Dominik Kowald, Markus Schedl +1
Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades accuracy. We address this tra…
Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems
Andrea Forster, Peter Müllner, Denis Helic +2
LLM agents are increasingly used for personalization due to their ability to communicate directly with users in natural language, integrate external knowledge bases, and negotiate…
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
Kevin Innerebner, Dominik Kowald, Markus Schedl +1
Recommender systems often rely on sub-symbolic machine learning approaches that operate as opaque black boxes. These approaches typically fail to account for the cognitive processe…
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems
Oleg Lesota, Jonas Geiger, Max Walder +2
Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training dat…
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…
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…
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…
Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings
Emanuel Lacic, Tomislav Duricic, Leon Fadljevic +2
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. To…
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…
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…
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…
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck +2
Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalize…
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…
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…
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…
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…