User Embeddings are Superpositions of Interpretable Behavioral Modes

This paper explores how sparse autoencoders can be applied to user embeddings to uncover hidden behavioral modes without needing raw interaction data or text-aligned training. By treating user representations as sparse superpositions of interpretable concepts, this post-hoc, model-agnostic framework enables zero-shot natural language audience retrieval and fine-grained characterization. Extensive validation on synthetic data, e-commerce records, and transformer-based sequential recommenders demonstrates that sparse autoencoders successfully recover underlying behavioral structures while outperforming classical dimensionality reduction methods in reconstruction fidelity, ranking quality, and feature diversity.