Fairness in Group Recommender Systems Using Variational Autoencoders

Abstract

Recommender systems are integral to enhancing user experiences on platforms, like Amazon and Netflix, by providing personalized suggestions. However, these systems often face significant fairness challenges, particularly in group settings where diverse preferences must be aggregated. In this paper, we explore the use of Variational Autoencoders (VAEs) to improve fairness in group recommendations. By introducing stochastic elements into the VAE framework, we aim to generate diverse and equitable recommendations. Extensive evaluations using the MovieLens 20M dataset demonstrate that incorporating noise during the recommendation process significantly enhances fairness with a minimal impact on ranking quality. The study identifies the Hybrid aggregation method paired with uniform noise as the optimal tradeoff, balancing group satisfaction, dissatisfaction, and ranking quality.