通过优化提示词提升小样本推荐效果，解决冷启动用户难题
Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users
- 设计上下文感知的提示生成方法，融合用户画像与支持集
- 在低数据场景下显著提升精确率和NDCG指标
- 适合研究大模型推荐系统与冷启动问题的从业者
冷启动用户因缺乏历史行为数据而严重影响推荐系统性能。本文提出一种针对少样本大语言模型（LLM）的指令提示优化方法，构建了从用户画像u和精选支持集Ds映射到预测项目排名R̂的提示生成框架。基于BioGPT、LLaMA-2、GPT-4等自回归式Transformer模型的系统性实验表明，在低数据条件下，通过最优示例注入与指令结构化，可显著提升模型的precision@k与NDCG得分。该方法采用标记级对齐与嵌入空间正则化，增强语义保真度。研究发现，提示词的适时构造不仅影响语法结构，更直接调控注意力尺度与解码行为，证明提示适配是应对大模型推荐中冷启动问题的有效路径。
原文摘要 · Abstract (English)
The cold-start user issue further compromises the effectiveness of recommender systems in limiting access to the historical behavioral information. It is an effective pipeline to optimize instructional prompts on a few-shot large language model (LLM) used in recommender tasks. We introduce a context-conditioned prompt formulation method P(u,\ Ds)\ \rightarrow\ R\widehat, where u is a cold-start user profile, Ds is a curated support set, and R\widehat is the predicted ranked list of items. Based on systematic experimentation with transformer-based autoregressive LLMs (BioGPT, LLaMA-2, GPT-4), we provide empirical evidence that optimal exemplar injection and instruction structuring can significantly improve the precision@k and NDCG scores of such models in low-data settings. The pipeline uses token-level alignments and embedding space regularization with a greater semantic fidelity. Our findings not only show that timely composition is not merely syntactic but also functional as it is in direct control of attention scales and decoder conduct through inference. This paper shows that prompt-based adaptation may be considered one of the ways to address cold-start recommendation issues in LLM-based pipelines.
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