Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction

TL;DR

This paper introduces Zipf Matrix Factorization, a novel approach that reduces the Matthew Effect in recommender systems, improving fairness while maintaining or enhancing traditional performance metrics.

Contribution

It proposes a new matrix factorization algorithm that explicitly addresses fairness issues caused by the Matthew Effect in recommendation systems.

Findings

Improves fairness by reducing Matthew Effect impact.

Enhances traditional recommendation metrics like MAE and precision@K.

Demonstrates effectiveness through experimental results.

Abstract

Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance such as MAE and precision@K. Major techniques such as matrix factorization and learning to rank are optimized based on such evaluation metrics. However, the intrinsic Matthew Effect problem poses great threat to the fairness of the recommender system, and the unfairness problem cannot be resolved by optimization of traditional metrics. In this paper, we propose a novel algorithm that incorporates Matthew Effect reduction with the matrix factorization framework. We demonstrate that our approach can boost the fairness of the algorithm and enhances performance evaluated by traditional metrics.