AI 中文总结
本研究提出后LLM范式的无训练推荐框架CoRRe，通过协同信号优化物品嵌入，在真实数据集上性能优于现有无训练方法，接近或超过有训练方法。
AI 中文摘要
大语言模型（LLM）已展现出用于无训练推荐的潜力，但LLM生成的用户兴趣通常过于宽泛，难以用于细粒度物品检索。现有方法以LLM前的方式将协同过滤（CF）信号融入候选重排序或提示增强，收益有限。我们提出CoRRe，一种采用后LLM范式的无训练推荐框架，该框架将CF信号注入LLM生成的物品表示，随后与LLM生成的用户兴趣匹配以完成排序。具体而言，CoRRe利用物品-物品共购图优化物品嵌入的方向，利用物品流行度优化其幅度。在真实世界数据集上的实验表明，CoRRe始终优于现有无训练方法，且与有训练方法相比具备可比或更优性能，无需任何模型训练或任务特定微调。
英文摘要
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.
CommentsPublished as a conference paper at CIKM 2026 (short)
