Work

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

Year: 2025

Type: conference-paper

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.

DOI: 10.1145/3701716.3715508

Authors: Jian Xu, Sichun Luo, Xiangyu Chen, Haoming Huang, Hanxu Hou, Linqi Song

Institutions: Tsinghua University, City University of Hong Kong, Dongguan University of Technology, City University of Hong Kong, Shenzhen Research Institute, Alibaba Group (China)

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Natural Science Foundation of China, Universitas Brawijaya

Awards: G3643945900, G708958969

OpenAlex ID: W4410637127

FWCI: 8.226

Cites: 13

Cited by: 7

Related to: 10