Recent work has shown that large language models (LLMs) can enhance recommender systems by integrating collaborative filtering (CF) signals through hybrid prompting. However, most existing CF-LLM frameworks collapse explicit ratings into implicit or positive-only feedback, discarding the ordinal structure that conveys fine-grained preference strength. As a result, these models struggle to exploit graded semantics and nuanced preference distinctions. We propose Ordinal Semantic Anchoring (OSA), a hybrid CF-LLM framework that explicitly incorporates preference strength by modeling interaction-level user feedback. OSA represents ordinal preference levels as numeric textual tokens and uses their token embeddings as semantic anchors to align user-item interaction representations in the LLM latent space. Through strength-aware alignment across ordinal levels, OSA preserves preference semantics when integrating collaborative signals with LLMs. Experiments on multiple real-world datasets demonstrate that OSA consistently outperforms existing baselines, particularly in pairwise preference evaluation, highlighting its effectiveness in modeling fine-grained user preferences over prior CF-LLM methods.

翻译：近期研究表明，大语言模型（LLMs）可通过混合提示技术整合协同过滤（CF）信号来增强推荐系统。然而，现有大多数CF-LLM框架将显式评分压缩为隐式或仅正向反馈，丢弃了蕴含细粒度偏好强度的序数结构。这导致模型难以利用分级语义与细微偏好差异。本文提出序数语义锚定（OSA）框架——一种通过建模交互级用户反馈来显式整合偏好强度的混合CF-LLM框架。OSA将序数偏好层级表示为数字文本令牌，并利用其令牌嵌入作为语义锚点，在LLM潜在空间中对齐用户-物品交互表征。通过跨序数层级的强度感知对齐，OSA在整合协同信号与LLM时保留了偏好语义。在多个真实世界数据集上的实验表明，OSA始终优于现有基线模型，尤其在成对偏好评估中表现突出，凸显了其相较于先前CF-LLM方法在细粒度用户偏好建模方面的有效性。