Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerce and social media. However, in real-world systems, most users interact with only a handful of items, while the majority of items are seldom consumed. These two issues, known as the long-tail user and long-tail item challenges, often pose difficulties for existing SRS. These challenges can adversely affect user experience and seller benefits, making them crucial to address. Though a few works have addressed the challenges, they still struggle with the seesaw or noisy issues due to the intrinsic scarcity of interactions. The advancements in large language models (LLMs) present a promising solution to these problems from a semantic perspective. As one of the pioneers in this field, we propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR). This framework utilizes semantic embeddings derived from LLMs to enhance SRS without adding extra inference load from LLMs. To address the long-tail item challenge, we design a dual-view modeling framework that combines semantics from LLMs and collaborative signals from conventional SRS. For the long-tail user challenge, we propose a retrieval augmented self-distillation method to enhance user preference representation using more informative interactions from similar users. To verify the effectiveness and versatility of our proposed enhancement framework, we conduct extensive experiments on three real-world datasets using three popular SRS models. The results show that our method surpasses existing baselines consistently, and benefits long-tail users and items especially. The implementation code is available at https://github.com/Applied-Machine-Learning-Lab/LLM-ESR.

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选择，并已在电子商务和社交媒体等多个领域得到应用。然而，在实际系统中，大多数用户仅与少数商品进行交互，而绝大多数商品则很少被消费。这两个问题，即长尾用户和长尾商品挑战，常常给现有的序列推荐系统带来困难。这些挑战可能对用户体验和商家收益产生不利影响，因此解决它们至关重要。尽管已有一些工作尝试应对这些挑战，但由于交互行为本身存在稀疏性，它们仍然难以克服"跷跷板"效应或噪声问题。大语言模型（LLMs）的进展为从语义角度解决这些问题提供了有前景的方案。作为该领域的先行者之一，我们提出了面向序列推荐的大语言模型增强框架（LLM-ESR）。该框架利用从大语言模型获得的语义嵌入来增强序列推荐系统，而无需引入大语言模型额外的推理负担。针对长尾商品挑战，我们设计了一个双视角建模框架，融合了大语言模型的语义信息和传统序列推荐系统的协同信号。对于长尾用户挑战，我们提出了一种检索增强的自蒸馏方法，利用相似用户的更具信息量的交互行为来增强用户偏好表示。为了验证所提增强框架的有效性和普适性，我们在三个真实世界数据集上使用三种主流的序列推荐系统模型进行了大量实验。结果表明，我们的方法始终优于现有基线，尤其对长尾用户和长尾商品有益。实现代码可在 https://github.com/Applied-Machine-Learning-Lab/LLM-ESR 获取。