ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation

2025-11-19

AI Summary

To address the challenges of cold-start item recommendation and weak modeling of co-purchase patterns in recommender systems, this paper proposes ItemRAG—the first LLM-based recommendation framework leveraging item-level Retrieval-Augmented Generation (RAG). Methodologically, it replaces conventional user-level retrieval with a novel item-level semantic-co-purchase joint retrieval mechanism, which jointly encodes semantic similarity and frequency-weighted co-purchase signals to precisely capture item-level collaborative relationships. Furthermore, it constructs co-purchase sequences as retrieval contexts to enhance LLMs’ zero-shot recommendation capability. Empirically, ItemRAG achieves up to a 43% improvement in Hit-Ratio@1 under zero-shot settings across multiple benchmark datasets, significantly outperforming user-centric baselines. Crucially, it maintains consistent superiority in both standard and cold-start scenarios, demonstrating robust generalization without task-specific fine-tuning.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Recently, large language models (LLMs) have been widely used as recommender systems, owing to their strong reasoning capability and their effectiveness in handling cold-start items. To better adapt LLMs for recommendation, retrieval-augmented generation (RAG) has been incorporated. Most existing RAG methods are user-based, retrieving purchase patterns of users similar to the target user and providing them to the LLM. In this work, we propose ItemRAG, an item-based RAG method for LLM-based recommendation that retrieves relevant items (rather than users) from item-item co-purchase histories. ItemRAG helps LLMs capture co-purchase patterns among items, which are beneficial for recommendations. Especially, our retrieval strategy incorporates semantically similar items to better handle cold-start items and uses co-purchase frequencies to improve the relevance of the retrieved items. Through extensive experiments, we demonstrate that ItemRAG consistently (1) improves the zero-shot LLM-based recommender by up to 43% in Hit-Ratio-1 and (2) outperforms user-based RAG baselines under both standard and cold-start item recommendation settings.

Problem

• Improving LLM-based recommendation systems using item-based retrieval
• Capturing co-purchase patterns among items for better recommendations
• Handling cold-start items through semantically similar item retrieval

Innovation

• Item-based RAG retrieves relevant items from co-purchase histories
• Uses semantic similarity to handle cold-start items effectively
• Leverages co-purchase frequencies to improve recommendation relevance