Ondrej Kassák

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Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g.(More)
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 multimedia items take long time to experience by users, thus users usually do not provide rich preference information. In this paper we analyze the minimal amount of ratings needs to be done by(More)
The context of a user is a notoriously researched topic in the recommender systems community. It greatly influences user preferences and respectively his/her behaviour. The research focuses on the actual influence affecting user and temporal preferences of users. These tell us what the user likes, but fail at describing his/her behavior. We believe that the(More)
Behavioral patterns represent repeating sequences or sets of actions, which website users often perform together. Such patterns can be used to identify user preferences, recommend interesting content to him, etc. For dynamic sites with fast changing content (e.g., news, social networks) we need to recognize such patterns in an online time. In this paper, we(More)
With the increasing use of internet, users are accessing information and services easily through various media like social communication, multimedia content, online shopping and banking services etc. It becomes challenging task to accurately identify and differentiate normal and suspicious user behavior. Various businesses need information of next user(More)
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