Search Results for author:
Found 28 papers, 14 papers with code
Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media
Understanding how online media frame issues is crucial due to their impact on public opinion.
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
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.
FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines
Revealing the framing of news articles is an important yet neglected task in information seeking and retrieval.
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective.
mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection
This paper presents the winning system for the zero-shot Spanish framing detection task, which also achieves competitive places in eight additional languages.
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.
ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process.
Position Paper on Simulating Privacy Dynamics in Recommender Systems
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.
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
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).
Predicting Music Relistening Behavior Using the ACT-R Framework
In this paper, we describe a psychology-informed approach to model and predict music relistening behavior that is inspired by studies in music psychology, which relate music preferences to human memory.
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes.
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
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.
Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
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.
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs.
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
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.
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering.
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
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.
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
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.
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
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.
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric
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.
The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
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.
Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations
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.
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints.
Exploiting weak ties in trust-based recommender systems using regular equivalence
Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust.
Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence
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.
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation.
