Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems

TL;DR

This paper presents A2G-DiffRec, a diffusion recommender system that adaptively guides itself to improve item fairness, balancing recommendation accuracy and exposure equity across item popularity levels.

Contribution

It introduces an adaptive autoguidance mechanism guided by a less-trained model, promoting fairer item exposure in diffusion recommender systems.

Findings

A2G-DiffRec improves item fairness across datasets.

The method achieves fairness with minimal accuracy loss.

Experimental results outperform existing baselines.

Abstract

Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we introduce A2G-DiffRec, a diffusion recommender that incorporates adaptive autoguidance, where the main model is guided by a less-trained version of itself. Instead of using a fixed guidance weight, A2G-DiffRec learns to adaptively weigh the outputs of the main and weak models during training, supervised by a fairness-aware regularization that promotes balanced exposure across items with different popularity levels. Experimental results on three public datasets show that A2G-DiffRec is effective in enhancing item-side fairness at a marginal cost of accuracy reduction compared to existing guided diffusion recommenders and other non-diffusion baselines.