Training-Free LLM-Based Recommendation with Post-LLM Item Refinement Using Collaborative Signals

• URL: http://arxiv.org/abs/2608.19665v1
• Date: Thu, 20 Aug 2026 06:01:59 GMT
• Title: Training-Free LLM-Based Recommendation with Post-LLM Item Refinement Using Collaborative Signals
• Abstract summary: CoRRe is a training-free recommendation framework with a post-LLM paradigm.<n>We propose CoRRe, a training-free recommendation framework with a post-LLM paradigm.
• Score: 36.81561019224549
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) have shown promise for training-free recommendation, but LLM-generated user interests are often too broad for fine-grained item retrieval. Existing methods incorporate collaborative filtering (CF) signals in a pre-LLM manner through candidate reranking or prompt augmentation, yielding limited gains. We propose CoRRe, a training-free recommendation framework with a post-LLM paradigm that injects CF signals into LLM-generated item representations, which are later matched with LLM-generated user interests for ranking. Specifically, CoRRe refines the directions of item embeddings using an item-item co-purchase graph and their magnitudes using item popularity. Experiments on real-world datasets show that CoRRe consistently outperforms existing training-free methods and achieves competitive or superior performance compared with training-based methods, without requiring any model training or task-specific fine-tuning.

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