The successful integration of large language models (LLMs) into recommendation systems has proven to be a major breakthrough in recent studies, paving the way for more generic and transferable recommendations. However, LLMs struggle to effectively utilize user and item IDs, which are crucial identifiers for successful recommendations. This is mainly due to their distinct representation in a semantic space that is different from the natural language (NL) typically used to train LLMs. To tackle such issue, we introduce ControlRec, an innovative Contrastive prompt learning framework for Recommendation systems. ControlRec treats user IDs and NL as heterogeneous features and encodes them individually. To promote greater alignment and integration between them in the semantic space, we have devised two auxiliary contrastive objectives: (1) Heterogeneous Feature Matching (HFM) aligning item description with the corresponding ID or user's next preferred ID based on their interaction sequence, and (2) Instruction Contrastive Learning (ICL) effectively merging these two crucial data sources by contrasting probability distributions of output sequences generated by diverse tasks. Experimental results on four public real-world datasets demonstrate the effectiveness of the proposed method on improving model performance.

翻译：大型语言模型成功融入推荐系统已被近期研究证明为重大突破，为更通用、可迁移的推荐方法开辟了道路。然而，语言模型难以有效利用用户和物品标识符，而这些标识符是成功推荐的关键。主要原因在于，这些标识符在语义空间中的表征与训练语言模型常用的自然语言存在显著差异。为解决该问题，我们提出了ControlRec——一种面向推荐系统的创新对比提示学习框架。ControlRec将用户标识符与自然语言视为异构特征并分别进行编码。为促进两者在语义空间中的对齐与融合，我们设计了两种辅助对比目标：（1）异构特征匹配：基于用户交互序列，对齐物品描述与其对应标识符或用户下一偏好的标识符；（2）指令对比学习：通过对比不同任务生成输出序列的概率分布，有效融合这两种关键数据源。在四个真实世界公开数据集上的实验结果证明了所提方法在提升模型性能方面的有效性。