The remarkable text understanding and generation capabilities of large language models (LLMs) have revitalized the field of general recommendation based on implicit user feedback. Rather than deploying LLMs directly as recommendation models, a more flexible paradigm leverages their ability to interpret users' historical interactions and semantic contexts to extract structured profiles that characterize user preferences. These profiles can be further transformed into actionable high-dimensional representations, serving as powerful signals to augment and strengthen recommendation models. However, the mechanism by which such profiles enhance recommendation performance within the feature space remains insufficiently understood. Moreover, existing studies predominantly rely on nonlinear alignment and fusion strategies to incorporate these profiles, which often lead to semantic loss and fail to fully exploit their potential. To address these limitations, we revisit profiles from a retrieval perspective and propose a simple yet effective recommendation framework built upon distribution shaping (ProMax) in this paper. We begin by employing dense retrieval to uncover the collaborative relationships between user and item profiles within the feature space. Based on this insight, we introduce a dual distribution-reshaping process, in which the profile distribution acts as a guiding signal to steer the recommendation model toward learning user preferences for unseen items beyond the scope of observed interactions. We apply ProMax to four classic recommendation methods on three public datasets. The results indicate that ProMax substantially improves base model performance and outperforms existing LLM-based recommendation approaches.

翻译：大语言模型（LLM）卓越的文本理解与生成能力，重新振兴了基于隐式用户反馈的通用推荐领域。相较于直接部署LLM作为推荐模型，一种更灵活的范式利用其解读用户历史交互与语义上下文的能力，提取表征用户偏好的结构化画像。这些画像可进一步转化为实用的高维表示，作为增强并强化推荐模型的有力信号。然而，此类画像在特征空间内提升推荐性能的机制仍缺乏充分理解。此外，现有研究主要采用非线性对齐与融合策略整合这些画像，这常导致语义损失，且未能充分开发其潜力。为解决这些局限，我们从检索视角重新审视画像，并提出一种基于分布塑形的简单而有效的推荐框架（ProMax）。首先，我们采用密集检索揭示特征空间中用户与物品画像之间的协作关系。基于这一洞见，我们引入双重分布重塑过程，其中画像分布作为引导信号，引导推荐模型学习用户对观测交互范围外的未见物品的偏好。我们将ProMax应用于四个经典推荐方法及三个公开数据集。结果表明，ProMax显著提升了基座模型性能，并优于现有基于LLM的推荐方法。