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.

提出了一种新的 ID 表示对齐框架 RA-Rec，该框架结合了预训练的 ID 嵌入和 LLM，通过创新的对齐模块和高效的调整方法实现了高效的基于 LLM 的推荐，相对于当前最先进的方法，取得了多达 3.0% 的 HitRate@100 改进。