A dynamic multi-level collaborative filtering method for improved recommendations

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 method that is based on positive and negative adjustments with its main purpose to improve the quality of the recommendations provided. The proposed method can be used in different domains that use collaborative filtering to improve the experience of the users. 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.