Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommenders instead leverage natural-language representations that are easier to interpret and integrate with downstream reasoning modules. This paper studies how to construct effective textual profiles for users and items, and how to align them for recommendation. A central difficulty is that the best profile format is not known a priori: manually designed templates can be brittle and misaligned with task objectives. Moreover, generating user and item profiles independently may produce descriptions that are individually plausible yet semantically inconsistent for a specific user--item pair. We propose Duet, an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. Duet follows a three-stage procedure: it first turns raw histories and metadata into compact cues, then expands these cues into paired profile prompts and then generate profiles, and finally optimizes the generation policy with reinforcement learning using downstream recommendation performance as feedback. Experiments on three real-world datasets show that Duet consistently outperforms strong baselines, demonstrating the benefits of template-free profile exploration and joint user-item textual alignment.

翻译：传统推荐系统将用户和物品表示为稠密向量，并在共享潜在空间中学习对齐以进行相关性估计。近期基于大语言模型的推荐器转而采用更易解释且能与下游推理模块集成的自然语言表示。本文研究如何构建有效的用户与物品文本特征，以及如何对齐这些特征以服务于推荐任务。核心难点在于最优特征格式并非先验可知：人工设计的模板可能僵化且与任务目标不一致。此外，独立生成用户和物品特征可能产生各自合理但针对特定用户-物品对存在语义不一致的描述。我们提出Duet——一种交互感知的特征生成器，能基于用户历史与物品证据联合生成用户和物品特征。该框架采用三阶段流程：首先将原始历史记录和元数据转化为紧凑线索，进而将这些线索扩展为配对特征提示并生成特征，最终利用下游推荐性能作为反馈，通过强化学习优化生成策略。在三个真实数据集上的实验表明，Duet持续优于强基线方法，验证了无模板特征探索与用户-物品文本联合对齐的优越性。