Bridging Textual Profiles and Latent User Embeddings for Personalization

2026-05-07

AI Summary

This work addresses the challenge of balancing interpretability and performance in personalized recommendation by proposing BLUE, a novel framework that unifies textual user profiles with implicit embedding representations. BLUE leverages reinforcement learning to align semantic text generated by large language models with embedding-based recommendation objectives. The approach jointly optimizes interpretability and retrieval effectiveness through an embedding-space reward and a text-space supervision signal, while also enabling cross-domain transfer. Experimental results demonstrate that BLUE significantly outperforms baseline methods on the Amazon Reviews 2023 and Google Local Reviews datasets, maintaining robust performance regardless of whether embeddings are frozen or trainable. Furthermore, it exhibits strong capabilities in cross-domain recommendation and personalized question-answering tasks.

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 the embedding space. We further introduce a text-space supervision signal based on next-item prediction, ensuring the learned profiles remain both semantically meaningful and highly effective for downstream retrieval. Experiments on Amazon Reviews 2023 and Google Local Reviews in zero-shot sequential recommendation settings demonstrate that BLUE consistently outperforms strong baselines under both frozen and trainable embedding conditions. Notably, BLUE achieves clear gains in cross-domain transfer, highlighting the strong generalization ability of the learned user profiles. Furthermore, these generated profiles provide superior personalized context for question answering compared to raw user histories or alternative profile optimization methods. Overall, these results show that BLUE provides an effective way to unify interpretable textual profiling with discriminative latent embeddings for personalization.

Problem

• personalization
• user representation
• textual profiles
• latent embeddings
• interpretability

Innovation

• reinforcement learning
• user profiling
• large language models
• personalized recommendation
• embedding alignment