Within the scope of this thesis, three different recommender systems were created, each based on two stores's datasets. Firstly, store datasets are described in detail. Then, the tools and techniques used to implement the systems are presented. After that, the thesis describes the process of implementing the systems in detail. Then, evaluation of implemented recommender systems follows, describing their accuracy. The most successful two strategies to recommend new products to users turned out to be based on product similarity. Next, recommender systems, developed in this thesis, were compared to adapted system, created in closely related thesis. Given the nature of the problem, the recommender systems, developed in the scope of this thesis, performed better. Lastly, the thesis is concluded with possible upgrades, which have a potential to make the systems more accurate.