68 citations · 189 across the 12 of their papers we have counts for
14 papers
What Price Fairness? Evaluating Energy - Fairness - Accuracy Trade-off in Recommender Systems
Abhirup Mitra, Oleg Lesota, Antonela Tommasel
Fairness-aware recommender systems aim to mitigate systematic imbalances in recommendation outcomes, including how visibility, relevance, and opportunities are distributed among us…
Training seeds and model-selection stability in recommender-system evaluation
Juan Manuel Rodriguez, Oleg Lesota, Antonela Tommasel
Recommender-system experiments often rely on a single random training seed, assuming that run-to-run stochasticity has limited impact on evaluation conclusions. This assumption is…
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…
Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
Zihan Li, Gustavo Escobedo, Marta Moscati +2
Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we intr…
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…
Spin-Wave Voices: Sonification of Nanoscale Spin Waves as an Engagement and Research Tool
Santa Pile, Oleg Lesota, Silvan David Peter +2
Magnonics is an emerging research field that addresses the use of spin waves (magnons), purely magnetic waves, for information transport and processing. Spin waves are a potential…
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas +4
Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial train…
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Christian Ganhör, David Penz, Navid Rekabsaz +2
Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, r…
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus +3
The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establish…
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…
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Oleg Lesota, Navid Rekabsaz, Daniel Cohen +3
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich litera…
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota +2
In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have cont…
TripClick: The Log Files of a Large Health Web Search Engine
Navid Rekabsaz, Oleg Lesota, Markus Schedl +2
Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularl…