What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals

• URL: http://arxiv.org/abs/2505.20730v2
• Date: Wed, 04 Jun 2025 00:54:43 GMT
• Title: What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals
• Authors: Shahrooz Pouryousef, Ali Montazeralghaem,
• Abstract summary: User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems.<n>It remains unclear whether large language models (LLMs) can effectively reason over this type of collaborative information.<n>We introduce a simple retrieval-augmented generation (RAG) method that enhances LLMs by grounding their predictions in structured interaction data.
• Score: 4.297070083645049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems. While recent work has explored the use of large language models (LLMs) for recommendation, it remains unclear whether LLMs can effectively reason over this type of collaborative information. In this paper, we conduct a systematic comparison between LLMs and classical matrix factorization (MF) models to assess LLMs' ability to leverage user-item interaction data. We further introduce a simple retrieval-augmented generation (RAG) method that enhances LLMs by grounding their predictions in structured interaction data. Our experiments reveal that current LLMs often fall short in capturing collaborative patterns inherent to MF models, but that our RAG-based approach substantially improves recommendation quality-highlighting a promising direction for future LLM-based recommenders.

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