The enormous development of the Internet, both in the geographical scale and in the area of using its possibilities in everyday life, determines the creation and collection of huge amounts of data. Due to the scale, it is not possible to analyse them using traditional methods, therefore it makes a necessary to use modern methods and techniques. Such methods are provided, among others, by the area of recommendations. The aim of this study is to present a new algorithm in the area of recommendation systems, the algorithm based on data from various sets of information, both static (categories of objects, features of objects) and dynamic (user behaviour).

翻译：互联网在地理规模及日常生活应用领域的巨大发展，决定了海量数据的产生与收集。由于数据规模庞大，传统方法无法对其进行分析，因此必须采用现代方法与技术。推荐领域提供了此类方法。本研究旨在提出一种推荐系统领域的新算法，该算法基于来自不同信息集合的数据，包括静态数据（对象类别、对象特征）和动态数据（用户行为）。