A dynamic multi-level collaborative filtering method for improved recommendations

TL;DR

This paper introduces a dynamic multi-level collaborative filtering approach that enhances recommendation accuracy and quality across various online platforms through positive and negative adjustments, validated by extensive experiments.

Contribution

It presents a novel multi-level collaborative filtering method incorporating positive and negative adjustments to improve recommendation quality in diverse domains.

Findings

Significant improvement in recommendation accuracy.

Effective across multiple real-world datasets.

Outperforms several existing collaborative filtering methods.

Abstract

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.