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，引发了将LLM与推荐系统相结合的新浪潮，即基于LLM的推荐系统（LLM-based RS）。现有方法通常归为两种主要范式：ID直接使用范式和ID翻译范式，其核心缺陷在于缺乏推荐知识及唯一性。为解决此限制，我们提出一种新范式——ID表示，它以互补方式将预训练的ID嵌入引入LLM。本文提出RA-Rec——一种用于基于LLM推荐的高效ID表示对齐框架，该框架与多种基于ID的方法及LLM架构兼容。具体而言，我们将ID嵌入视为软提示，设计了一种创新的对齐模块，并针对对齐任务构建了定制数据的高效微调方法。大量实验表明，RA-Rec显著优于当前最先进方法，在利用少于10倍训练数据的条件下，可实现高达3.0%的绝对HitRate@100提升。