Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems

TL;DR

This paper introduces a post-hoc interpretability method using a Sparse Autoencoder to identify and mitigate popularity bias in recommender systems, improving fairness while maintaining recommendation accuracy.

Contribution

It presents a novel SAE-based approach that interprets and adjusts popularity signals in deep models, enhancing fairness with transparency and control.

Findings

Significantly improves fairness in recommendations

Maintains high recommendation accuracy

Provides interpretability and fine-grained control

Abstract

Popularity bias is a well-known challenge in recommender systems, where a small number of popular items receive disproportionate attention, while the majority of less popular items are largely overlooked. This imbalance often results in reduced recommendation quality and unfair exposure of items. Although existing mitigation techniques address this bias to some extent, they typically lack transparency in how they operate. In this paper, we propose a post-hoc method using a Sparse Autoencoder (SAE) to interpret and mitigate popularity bias in deep recommendation models. The SAE is trained to replicate a pre-trained model's behavior while enabling neuron-level interpretability. By introducing synthetic users with clear preferences for either popular or unpopular items, we identify neurons encoding popularity signals based on their activation patterns. We then adjust the activations of the most biased neurons to steer recommendations toward fairer exposure. Experiments on two public datasets using a sequential recommendation model show that our method significantly improves fairness with minimal impact on accuracy. Moreover, it offers interpretability and fine-grained control over the fairness-accuracy trade-off.