  Collaborative filtering is one of the most used approaches for providing
recommendations in various online environments. Even though collaborative
recommendation methods have been widely utilized due to their simplicity and
ease of use, accuracy is still an issue. In this paper we propose a multi-level
recommendation method with its main purpose being to assist users in decision
making by providing recommendations of better quality. The proposed method can
be applied in different online domains that use collaborative recommender
systems, thus improving the overall user experience. The efficiency of the
proposed method is shown by providing an extensive experimental evaluation
using five real datasets and with comparisons to alternatives.
