  One of the most used approaches for providing recommendations in various
online environments such as e-commerce is collaborative filtering. Although,
this is a simple method for recommending items or services, accuracy and
quality problems still exist. Thus, we propose a dynamic multi-level
collaborative filtering method that improves the quality of the
recommendations. The proposed method is based on positive and negative
adjustments and can be used in different domains that utilize collaborative
filtering to increase the quality of the user experience. Furthermore, the
effectiveness of the proposed method is shown by providing an extensive
experimental evaluation based on three real datasets and by comparisons to
alternative methods.
