KL-Mat : Fair Recommender System via Information Geometry

TL;DR

KL-Mat is a novel regularization-based recommender system leveraging information geometry to enhance fairness and accuracy, providing a robust, fast, and explainable solution to address sparsity and fairness issues.

Contribution

Introduces KL-Mat, a new regularization framework based on information geometry that improves fairness and accuracy in recommender systems.

Findings

KL-Mat achieves better fairness than vanilla matrix factorization.

The algorithm improves accuracy metrics like MAE.

KL-Mat is fast, easy to implement, and explainable.

Abstract

Recommender system has intrinsic problems such as sparsity and fairness. Although it has been widely adopted for the past decades, research on fairness of recommendation algorithms has been largely neglected until recently. One important paradigm for resolving the issue is regularization. However, researchers have not been able to come up with a consensusly agreed regularization term like regularization framework in other fields such as Lasso or Ridge Regression. In this paper, we borrow concepts from information geometry and propose a new regularization-based fair algorithm called KL-Mat. The algorithmic technique leads to a more robust performance in accuracy performance such as MAE. More importantly, the algorithm produces much fairer results than vanilla matrix factorization approach. KL-Mat is fast, easy-to-implement and explainable.