A multi-level collaborative filtering method that improves recommendations

TL;DR

This paper introduces a multi-level collaborative filtering approach designed to enhance recommendation accuracy across various online platforms, aiming to improve user decision-making and overall experience.

Contribution

It proposes a novel multi-level collaborative filtering method that significantly improves recommendation quality compared to existing approaches.

Findings

Enhanced recommendation accuracy demonstrated across five real datasets.

The method outperforms traditional collaborative filtering techniques.

Improved user decision support in diverse online environments.

Abstract

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.