Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the high inference latency of LLMs significantly restricts their practical deployment. To address this issue, this work investigates knowledge distillation from cumbersome LLM-based recommendation models to lightweight conventional sequential models. It encounters three challenges: 1) the teacher's knowledge may not always be reliable; 2) the capacity gap between the teacher and student makes it difficult for the student to assimilate the teacher's knowledge; 3) divergence in semantic space poses a challenge to distill the knowledge from embeddings. To tackle these challenges, this work proposes a novel distillation strategy, DLLM2Rec, specifically tailored for knowledge distillation from LLM-based recommendation models to conventional sequential models. DLLM2Rec comprises: 1) Importance-aware ranking distillation, which filters reliable and student-friendly knowledge by weighting instances according to teacher confidence and student-teacher consistency; 2) Collaborative embedding distillation integrates knowledge from teacher embeddings with collaborative signals mined from the data. Extensive experiments demonstrate the effectiveness of the proposed DLLM2Rec, boosting three typical sequential models with an average improvement of 47.97%, even enabling them to surpass LLM-based recommenders in some cases.

翻译：由于其强大的语义推理能力，大型语言模型（LLM）已被有效用作推荐系统，并取得了令人瞩目的性能。然而，LLM的高推理延迟严重限制了其实际部署。为解决该问题，本研究探索了从基于LLM的繁琐推荐模型向轻量级传统序列模型的知识蒸馏技术。该过程面临三大挑战：1）教师模型的知识并非始终可靠；2）教师与学生模型之间的能力差距使得学生难以吸收教师知识；3）语义空间差异对从嵌入中蒸馏知识构成挑战。针对这些挑战，本文提出了一种创新的蒸馏策略DLLM2Rec，专门用于将基于LLM的推荐模型知识迁移至传统序列模型。DLLM2Rec包含：1）重要性感知排序蒸馏模块，通过根据教师置信度与学生-教师一致性对实例进行加权，过滤出可靠且适合学生模型的知识；2）协同嵌入蒸馏模块，将教师嵌入中的知识与从数据中挖掘的协同信号相整合。大量实验证明，所提出的DLLM2Rec具有显著效果，可将三种典型序列模型的性能平均提升47.97%，甚至在某些情况下使其超越基于LLM的推荐系统。