发表机构
Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LLM序列推荐中提示敏感性问题，提出LRPRec框架，通过个性化提示注入与语义漂移约束，在三个基准上超越强基线并免去手动调优。
AI 中文摘要
LLM驱动的序列推荐将下一项预测表述为基于自然语言提示的自回归生成。然而，语义等价的提示中微小的措辞变化可能导致性能大幅波动，削弱鲁棒性并需要昂贵的人工提示工程。连续提示学习减少了对模板的依赖，但面临两个相互作用的挑战：共享的任务级指令缺乏用户特定的推理指导，而梯度更新可能将连续提示推离LLM的有效语义空间。注入个性化信号可能进一步加剧这种语义漂移。为解决这些挑战，我们提出LRPRec，一个可学习的提示框架，从离散模板初始化连续指令提示，并引入两种互补机制。个性化提示注入将用户行为编码为偏好嵌入，并将其加性注入共享提示，实现参数高效的用户级适应。语义漂移约束将共享提示正则化在其初始化锚点周围的信任区域内，以在优化过程中保持语义有效性。通过约束共享组件同时允许加性个性化，LRPRec将稳定性与表达性解耦。在三个基准数据集上的大量实验表明，相对于强基线有一致的改进，同时消除了对背景和任务推理模板进行手动调优的需求。
英文摘要
LLM-driven sequential recommendation formulates next-item prediction as autoregressive generation conditioned on natural-language prompts. However, minor wording changes in semantically equivalent prompts can cause substantial performance fluctuations, undermining robustness and requiring costly manual prompt engineering. Continuous prompt learning reduces template dependence but faces two interacting challenges: shared task-level instructions lack user-specific reasoning guidance, while gradient updates can push continuous prompts outside the LLM's effective semantic space. Injecting personalized signals can further amplify this semantic drift. To address these challenges, we propose LRPRec, a learnable prompting framework that initializes continuous instruction prompts from discrete templates and introduces two complementary mechanisms. Personalized prompt injection encodes user behavior into a preference embedding and additively injects it into shared prompts, enabling parameter-efficient user-level adaptation. A semantic drift constraint regularizes the shared prompts within a trust region around their initialization anchors to preserve semantic validity during optimization. By constraining the shared component while allowing additive personalization, LRPRec decouples stability from expressiveness. Extensive experiments on three benchmark datasets demonstrate consistent improvements over strong baselines while eliminating the need for manual tuning of background and task inference templates.
