让大模型只在缺知识时补信息，提升推荐效果
Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
- 根据模型对协同关系的识别能力，选择性补充缺失知识
- 在四个真实数据集上同时提升推荐准确率与上下文利用率
- 无需微调，适合想优化大模型推荐效率的研究者
大语言模型（LLMs）作为无训练推荐系统展现出强大能力，但其对不同物品的知识掌握程度不均，存在知识缺口问题。现有方法通常对所有物品统一添加外部信息，既浪费有限上下文空间，又可能干扰模型推理。为此，我们提出KnowSA_CKP（基于对比知识探测的选择性知识增强），通过评估模型捕捉协同关系的能力，判断哪些物品真正需要知识补充，并仅在必要时注入额外信息。该方法避免了对已知物品的冗余增强，更高效利用上下文预算。实验表明，KnowSA_CKP无需微调，在四个真实数据集上均显著提升推荐准确率与上下文效率。代码已开源。
原文摘要 · Abstract (English)
Large language models (LLMs) have recently emerged as powerful training-free recommenders. However, their knowledge of individual items is inevitably uneven due to imbalanced information exposure during pretraining, a phenomenon we refer to as knowledge gap problem. To address this, most prior methods have employed a naive uniform augmentation that appends external information for every item in the input prompt. However, this approach not only wastes limited context budget on redundant augmentation for well-known items but can also hinder the model's effective reasoning. To this end, we propose KnowSA_CKP (Knowledge-aware Selective Augmentation with Comparative Knowledge Probing) to mitigate the knowledge gap problem. KnowSA_CKP estimates the LLM's internal knowledge by evaluating its capability to capture collaborative relationships and selectively injects additional information only where it is most needed. By avoiding unnecessary augmentation for well-known items, KnowSA_CKP focuses on items that benefit most from knowledge supplementation, thereby making more effective use of the context budget. KnowSA_CKP requires no fine-tuning step, and consistently improves both recommendation accuracy and context efficiency across four real-world datasets. Our code is available at https://github.com/nowhyun/KnowSA\_CKP.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。