57 citations · 295 across the 30 of their papers we have counts for
9 papers · 1 filter
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…
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…
The Cold-start Problem: Minimal Users' Activity Estimation
Juraj Visnovsky, Ondrej Kassak, Michal Kompan +1
Cold-start problem, which arises upon the new users arrival, is one of the fundamental problems in today's recommender approaches. Moreover, in some domains as TV or multime-dia-it…
Session-based k-NNs with Semantic Suggestions for Next-item Prediction
Miroslav Rac, Michal Kompan, Maria Bielikova
One of the most critical problems in e-commerce domain is the information overload problem. Usually, an enormous number of products is offered to a user. The characteristics of thi…