RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
This work addresses the problem of improving recommendation systems for users by enhancing the effectiveness of large language models, which is an incremental improvement over existing retrieval augmented generation methods.
The authors tackled the problem of improving retrieval augmented large language model recommendation by incorporating representation learning, resulting in enhanced system performance. The proposed method, RALLRec, was validated through extensive experiments on three real-world datasets.
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.