RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning

TL;DR

RALLRec enhances retrieval-augmented recommendation systems by combining detailed textual and collaborative representations, incorporating user interest dynamics, and demonstrating improved performance on real-world datasets.

Contribution

The paper introduces RALLRec, a novel method that jointly learns textual and collaborative representations for better retrieval in LLM-based recommendation systems.

Findings

Improved recommendation accuracy on three real-world datasets

Effective incorporation of user interest dynamics

Validated superiority over existing methods

Abstract

Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further incorporated into these systems to retrieve more relevant items and improve system performance. However, existing RAG methods rely primarily on textual semantics and often fail to incorporate the most relevant items, limiting the effectiveness of the systems. In this paper, we propose Representation learning for retrieval-Augmented Large Language model Recommendation (RALLRec). Specifically, we enhance textual semantics by prompting LLMs to generate more detailed item descriptions, followed by joint representation learning of textual and collaborative semantics, which are extracted by the LLM and recommendation models, respectively. Considering the potential time-varying characteristics of user interest, a simple yet effective reranking method is further introduced to capture the dynamics of user preference. We conducted extensive experiments on three real-world datasets, and the evaluation results validated the effectiveness of our method. Code is made public at https://github.com/JianXu95/RALLRec.