User embeddings contain interpretable behavioral patterns that can be extracted post-hoc using sparse autoencoders and automatically labeled—making opaque recommendation systems more transparent without changing existing infrastructure.
User embeddings in recommendation systems are dense and hard to interpret. This paper shows that embeddings can be decomposed into sparse, interpretable behavioral modes using sparse autoencoders. The recovered modes can be automatically labeled in natural language, enabling zero-shot audience retrieval without retraining the original system.