The development of the internet and technology has caused a huge increase in data that has become untenable for the user. Solving this […]
PeWe.Data OA-2018
Meta-recommending: Adaptive Selection of Personalized Recommendation Algorithms
Recommender systems have become essential part of the Web in many domains. Research in this topic in the last few years led to design […]
Hybrid recommendation
Recommendation is scientific department which importance correlates with the number of options user have when he wants to choose a product in random […]
Personalized Product Recommendation for Users
This work deals with the issue of personalized recommendations to users in the domain of multimedia content, focusing on the adaptive selection of […]
Predicting Offer Popularity in E-commerce Environment
Increasing popularity of buying goods online causes a rapid growth of a number of e-shops. Many different sellers often offer many similar products […]
Analysis of User Feedback
Marek Wallner bachelor study, supervised by Peter Gašpar User working on web leaves traces behind.Traces can be conscious(explicit feedback) or unconscious(implicit feedback). As explicit […]
User behavior on the Web: prediction of retention
Predicting customer behavior represents a significant role in the dynamic web environment. Knowing customer satisfaction with services, content or goods is a good […]
Recommendation Taking into Account the Time Aspects of Users and Items
This work deals with time-aware recommender systems in a domain of location-based social networks, such as Yelp or Foursquare. We propose a novel […]
Interpretability of Neural Network Models Used in Data Analysis
Interpretability is an integral part of every machine learning model. Without it, the chance our model will be used in domains where the […]
Interpretability of machine learning models created by clustering algorithms
In our era, machine learning has become something of a certainty for solving many research problems, but also problems from real life. The […]