Bridging Textual Profiles and Latent User Embeddings for Personalization
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. To bridge this gap, we present BLUE, a reinforcement learning framework that unifies these two forms of user representation by aligning language-based user profiles with embedding-based recommendation objectives. Given a user interaction history, BLUE leverages a profiler Large Language Model (LLM...
| Authors | Zhaoxuan Tan, Xiang Zhai, Yan Zhu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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