57 citations · 134 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…