11 citations · 27 across the 12 of their papers we have counts for
7 papers · 1 filter
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization
Shahed Masoudian, Markus Frohmann, Navid Rekabsaz +1
Language models frequently inherit societal biases from their training data. Numerous techniques have been proposed to mitigate these biases during both the pre-training and fine-t…
The Importance of Cognitive Biases in the Recommendation Ecosystem
Markus Schedl, Oleg Lesota, Stefan Brandl +3
Cognitive biases have been studied in psychology, sociology, and behavioral economics for decades. Traditionally, they have been considered a negative human trait that leads to inf…
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…
Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach
Muhammad Saad Saeed, Shah Nawaz, Muhammad Zaigham Zaheer +6
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that…
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…
Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan
Muhammad Saad Saeed, Shah Nawaz, Muhammad Salman Tahir +7
The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, the audio-visual systems are one of the widely used multimo…
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…