Managing multi-facet bias in collaborative filtering recommender systems

TL;DR

This paper introduces MFAIR, a post-processing algorithm designed to reduce intersectional geographical and popularity biases in collaborative filtering recommender systems while maintaining recommendation accuracy.

Contribution

The paper presents MFAIR, a novel multi-facet bias mitigation algorithm specifically targeting intersectional biases in collaborative filtering, with extensive real-world dataset validation.

Findings

MFAIR effectively reduces geographical and popularity biases.

The algorithm maintains a balance between bias mitigation and recommendation accuracy.

MFAIR outperforms existing bias mitigation methods with minimal efficiency loss.

Abstract

Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the system to be biased against a series of items with certain attributes. Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. We introduce an algorithm called MFAIR, a multi-facet post-processing bias mitigation algorithm to alleviate these biases. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases. According to the results, our proposed approach outperforms a well-known competitor with no or only a slight loss of efficiency.