Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text semantics, usually neglecting the valuable collaborative information from user-item interactions in recommendations. While these text-emphasizing approaches excel in cold-start scenarios, they may yield sub-optimal performance in warm-start situations. In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation. CoLLM captures collaborative information through an external traditional model and maps it to the input token embedding space of LLM, forming collaborative embeddings for LLM usage. Through this external integration of collaborative information, CoLLM ensures effective modeling of collaborative information without modifying the LLM itself, providing the flexibility to employ various collaborative information modeling techniques. Extensive experiments validate that CoLLM adeptly integrates collaborative information into LLMs, resulting in enhanced recommendation performance. We release the code and data at https://github.com/zyang1580/CoLLM.

翻译：利用大型语言模型作为推荐系统（LLMRec）已引起广泛关注，并为用户偏好建模带来了全新视角。现有LLMRec方法优先处理文本语义，通常忽略推荐中用户-项目交互的宝贵协同信息。虽然这些侧重文本的方法在冷启动场景中表现优异，但在热启动情况下可能产生次优结果。为在冷启动和热启动场景中均实现更优推荐，我们提出CoLLM——一种创新性LLMRec方法，能够将协同信息无缝整合至大型语言模型中以实现推荐。CoLLM通过外部传统模型捕获协同信息，并将其映射至LLM的输入词元嵌入空间，形成供LLM使用的协同嵌入。通过这种协同信息的外部集成，CoLLM在无需修改LLM本身的前提下有效建模协同信息，从而具备采用多种协同信息建模技术的灵活性。大量实验证明，CoLLM能够巧妙地将协同信息融入LLM，显著提升推荐性能。我们已在https://github.com/zyang1580/CoLLM 公开代码与数据。