Bridging Textual Profiles and Latent User Embeddings for Personalization
Zhaoxuan Tan, Xiang Zhai, Yan Zhu, Meng Jiang, Mohamed Hammad

TL;DR
BLUE is a reinforcement learning framework that aligns textual user profiles with embedding-based recommendation objectives, improving personalization and interpretability in recommendation systems.
Contribution
BLUE unifies textual user profiles with latent embeddings using reinforcement learning, enabling interpretable and effective user representations for recommendation tasks.
Findings
BLUE outperforms strong baselines in zero-shot recommendation settings.
BLUE achieves better cross-domain transfer of user profiles.
Generated profiles enhance question answering compared to raw histories.
Abstract
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) to generate textual profiles, while an embedding model provides reward signals. This encourages the resulting textual representations to move closer to positive items and farther from negative ones in…
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