Sequential recommendation systems (SRS) serve the purpose of predicting users' subsequent preferences based on their past interactions and have been applied across various domains such as e-commerce and social networking platforms. However, practical SRS encounters challenges due to the fact that most users engage with only a limited number of items, while the majority of items are seldom consumed. These challenges, termed as the long-tail user and long-tail item dilemmas, often create obstacles for traditional SRS methods. Mitigating these challenges is crucial as they can significantly impact user satisfaction and business profitability. While some research endeavors have alleviated these issues, they still grapple with issues such as seesaw or noise stemming from the scarcity of interactions. The emergence of large language models (LLMs) presents a promising avenue to address these challenges from a semantic standpoint. In this study, we introduce the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR), which leverages semantic embeddings from LLMs to enhance SRS performance without increasing computational overhead. To combat the long-tail item challenge, we propose a dual-view modeling approach that fuses semantic information from LLMs with collaborative signals from traditional SRS. To address the long-tail user challenge, we introduce a retrieval augmented self-distillation technique to refine user preference representations by incorporating richer interaction data from similar users. Through comprehensive experiments conducted on three authentic datasets using three widely used SRS models, our proposed enhancement framework demonstrates superior performance compared to existing methodologies.

翻译：序列推荐系统（SRS）旨在根据用户的历史交互行为预测其后续偏好，并已广泛应用于电子商务和社交网络平台等多个领域。然而，实际应用中，由于大多数用户仅与有限数量的物品产生交互，而绝大多数物品很少被消费，SRS面临着严峻挑战。这些被称为长尾用户与长尾物品困境的问题，往往对传统SRS方法构成障碍。缓解这些挑战至关重要，因为它们会显著影响用户满意度和商业收益。尽管已有研究尝试缓解这些问题，但仍受困于交互稀疏性带来的跷跷板效应或噪声干扰。大语言模型（LLMs）的出现为从语义角度解决这些挑战提供了新途径。本研究提出大语言模型增强的序列推荐框架（LLM-ESR），该框架利用LLMs的语义嵌入来提升SRS性能，且不增加计算开销。针对长尾物品问题，我们提出双视角建模方法，将LLMs的语义信息与传统SRS的协同信号相融合。针对长尾用户问题，我们引入检索增强的自蒸馏技术，通过整合相似用户更丰富的交互数据来优化用户偏好表征。通过在三个真实数据集上使用三种广泛应用的SRS模型进行综合实验，我们提出的增强框架展现出优于现有方法的性能表现。