Bridging Textual Profiles and Latent User Embeddings for Personalization

ArXi:2605.06981v1 Announce Type: cross Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typically employ either supervised latent user embeddings, which are effective for retrieval but difficult to interpret, or textual user profiles, which are interpretable but challenging to optimize for downstream utility due to lack of direct supervision.