Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet existing integration approaches create prohibitive inference costs in real time. To address these limitations, we present a novel knowledge distillation method that utilizes textual user profile generated by pre-trained LLMs into sequential recommenders without requiring LLM inference at serving time. The resulting approach maintains the inference efficiency of traditional sequential models while requiring neither architectural modifications nor LLM fine-tuning.

翻译：序列推荐系统在时间维度用户行为建模方面取得了显著成功，但在捕捉交互模式之外的丰富用户语义方面仍存在局限。大语言模型凭借其推理能力为增强用户理解提供了机遇，但现有集成方法在实际应用中产生了难以承受的实时推理成本。为解决这些限制，我们提出了一种新颖的知识蒸馏方法，该方法利用预训练大语言模型生成的文本化用户画像，在服务阶段无需大语言模型推理即可将知识融入序列推荐器。所提出的方法在保持传统序列模型推理效率的同时，既无需修改模型架构，也无需对大语言模型进行微调。