A dynamic multi-level collaborative filtering method for improved recommendations
This incremental improvement addresses recommendation quality issues for users in domains like e-commerce that rely on collaborative filtering.
The authors tackled accuracy and quality problems in collaborative filtering for recommendations by proposing a dynamic multi-level method based on positive and negative adjustments, showing effectiveness through experiments on three real datasets with comparisons to alternative methods.
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.