57 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Brno University of TechnologyCZ13 papers
- Slovak University of Technology in BratislavaSK6 papers
- Adobe Systems (United States)US3 papers
- Comenius University BratislavaSK3 papers
- Amazon (Germany)DE2 papers
- University of SheffieldGB2 papers
- Agence France-PresseFR1 paper
- Amazon (United Kingdom)GB1 paper
- Amazon (United States)US1 paper
- Amsterdam University of the ArtsNL1 paper
- Central European UniversityAT1 paper
- Fondazione Bruno KesslerIT1 paper
10 papers · 1 filter
Algorithmic Audit of Personalisation Drift in Polarising Topics on TikTok
Branislav Pecher, Adrian Bindas, Jan Jakubcik +8
Social media platforms have become an integral part of everyday life, serving as a primary source of news and information for many users. These platforms increasingly rely on perso…
From Latent to Observable Position-Based Click Models in Carousel Interfaces
Santiago de Leon-Martinez, Robert Moro, Branislav Kveton +1
Click models are a central component of learning and evaluation in recommender systems, yet most existing models are designed for single ranked list interfaces. In contrast, modern…
Riding the Carousel: The First Extensive Eye Tracking Analysis of Browsing Behavior in Carousel Recommenders
Santiago de Leon-Martinez, Robert Moro, Branislav Kveton +1
Carousels have become the de-facto standard user interface in online services. However, there is a lack of research in carousels, particularly examining how recommender systems may…
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
Santiago de Leon-Martinez, Jingwei Kang, Robert Moro +4
Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and rec…
Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings
Matej Mosnar, Adam Skurla, Branislav Pecher +5
Social media platforms are constantly shifting towards algorithmically curated content based on implicit or explicit user feedback. Regulators, as well as researchers, are calling…
Context-aware adaptive personalised recommendation: a meta-hybrid
Peter Tibensky, Michal Kompan
Recommenders take place on a wide scale of e-commerce systems, reducing the problem of information overload. The most common approach is to choose a recommender used by the system…
Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis
Santiago de Leon-Martinez, Robert Moro, Maria Bielikova
Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking d…
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
Ivan Srba, Robert Moro, Matus Tomlein +8
In this paper, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it t…
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
Matus Tomlein, Branislav Pecher, Jakub Simko +7
The negative effects of misinformation filter bubbles in adaptive systems have been known to researchers for some time. Several studies investigated, most prominently on YouTube, h…
Exploring Customer Price Preference and Product Profit Role in Recommender Systems
Michal Kompan, Peter Gaspar, Jakub Macina +2
Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. Howeve…