Recommender or recommendation systems form a subclass of information filtering systems. Their main purpose is to produce lists of recommendations for individual users. There are two methods used at the heart of the process: collaborative and content-based filtering. The first one relies on user’s past behavior and similar decisions made by other customers. The second one uses a series of discrete characteristics of an item in order to create a list of additional items with similar properties. Both approaches are often combined to create more complex system. Thanks to personal recommendation services and engines, e-commerce is now based on individual approach to every customer: products are marketed to individuals based on their unique needs.