Item-based Variational Auto-encoder for Fair Music Recommendation

TL;DR

This paper introduces an ensemble recommender system combining item-based variational auto-encoders with fairness regularization and BPRMF, effectively reducing popularity bias and improving recommendation fairness and accuracy.

Contribution

It proposes a novel ensemble approach using item-based VAE with fairness regularization and introduces a new fairness evaluation metric.

Findings

Item-based VAE with fairness regularization reduces popularity bias.

Ensemble improves top-1 recommendation accuracy.

Proposed coefficient variance based fairness metric enhances evaluation.

Abstract

We present our solution for the EvalRS DataChallenge. The EvalRS DataChallenge aims to build a more realistic recommender system considering accuracy, fairness, and diversity in evaluation. Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF). To mitigate the bias in popularity, we use an item-based VAE for each popularity group with an additional fairness regularization. To make a reasonable recommendation even the predictions are inaccurate, we combine the recommended list of BPRMF and that of item-based VAE. Through the experiments, we demonstrate that the item-based VAE with fairness regularization significantly reduces popularity bias compared to the user-based VAE. The ensemble between the item-based VAE and BPRMF makes the top-1 item similar to the ground truth even the predictions are inaccurate. Finally, we propose a `Coefficient Variance based Fairness' as a novel evaluation metric based on our reflections from the extensive experiments.