Large language models (LLM) have recently emerged as a powerful tool for a variety of natural language processing tasks, bringing a new surge of combining LLM with recommendation systems, termed as LLM-based RS. Current approaches generally fall into two main paradigms, the ID direct usage paradigm and the ID translation paradigm, noting their core weakness stems from lacking recommendation knowledge and uniqueness. To address this limitation, we propose a new paradigm, ID representation, which incorporates pre-trained ID embeddings into LLMs in a complementary manner. In this work, we present RA-Rec, an efficient ID representation alignment framework for LLM-based recommendation, which is compatible with multiple ID-based methods and LLM architectures. Specifically, we treat ID embeddings as soft prompts and design an innovative alignment module and an efficient tuning method with tailored data construction for alignment. Extensive experiments demonstrate RA-Rec substantially outperforms current state-of-the-art methods, achieving up to 3.0% absolute HitRate@100 improvements while utilizing less than 10x training data.

翻译：大语言模型（LLM）近期已成为多种自然语言处理任务的强大工具，由此催生了将LLM与推荐系统相结合的新浪潮，即LLM-based RS。当前方法主要分为两种范式：直接使用ID的范式和ID翻译范式，其核心缺陷在于缺乏推荐知识及唯一性。为解决这一局限，我们提出了一种新范式——ID表示，该范式以互补方式将预训练的ID嵌入融入LLM。本文中，我们提出RA-Rec——一个面向基于LLM推荐的高效ID表示对齐框架，该框架兼容多种基于ID的方法和LLM架构。具体而言，我们将ID嵌入视为软提示，并设计了创新的对齐模块以及一种结合定制数据构建的高效调优方法。大量实验表明，RA-Rec显著优于当前最先进的方法：在使用不到10倍训练数据的情况下，HitRate@100绝对值提升最高达3.0%。