Sequential recommendation (SR) tasks enhance recommendation accuracy by capturing the connection between users' past interactions and their changing preferences. Conventional models often focus solely on capturing sequential patterns within the training data, neglecting the broader context and semantic information embedded in item titles from external sources. This limits their predictive power and adaptability. Recently, large language models (LLMs) have shown promise in SR tasks due to their advanced understanding capabilities and strong generalization abilities. Researchers have attempted to enhance LLMs' recommendation performance by incorporating information from SR models. However, previous approaches have encountered problems such as 1) only influencing LLMs at the result level;2) increased complexity of LLMs recommendation methods leading to reduced interpretability; 3) incomplete understanding and utilization of SR models information by LLMs. To address these problems, we proposes a novel framework, DELRec, which aims to extract knowledge from SR models and enable LLMs to easily comprehend and utilize this supplementary information for more effective sequential recommendations. DELRec consists of two main stages: 1) SR Models Pattern Distilling, focusing on extracting behavioral patterns exhibited by SR models using soft prompts through two well-designed strategies; 2) LLMs-based Sequential Recommendation, aiming to fine-tune LLMs to effectively use the distilled auxiliary information to perform SR tasks. Extensive experimental results conducted on three real datasets validate the effectiveness of the DELRec framework.

翻译：序列推荐任务通过捕捉用户历史交互与动态偏好之间的关联来提升推荐准确性。传统模型通常仅关注训练数据中的序列模式，忽略了外部来源（如商品标题）所蕴含的广泛上下文与语义信息，这限制了其预测能力与适应性。近年来，大语言模型凭借其先进的理解能力与强大的泛化性能，在序列推荐任务中展现出潜力。研究者尝试通过融合序列推荐模型的信息来提升大语言模型的推荐性能，但现有方法存在以下问题：1) 仅能在结果层面影响大语言模型；2) 推荐方法复杂度增加导致可解释性降低；3) 大语言模型对序列推荐模型信息的理解与利用不充分。为解决上述问题，本文提出一种新颖框架DELRec，旨在从序列推荐模型中提取知识，并使大语言模型能够轻松理解并利用这些补充信息以实现更有效的序列推荐。DELRec包含两个核心阶段：1) 序列推荐模型模式蒸馏——通过两种精心设计的策略，利用软提示提取序列推荐模型表现出的行为模式；2) 基于大语言模型的序列推荐——通过微调使大语言模型能有效利用蒸馏得到的辅助信息执行序列推荐任务。在三个真实数据集上的大量实验结果验证了DELRec框架的有效性。