用大模型提升推荐系统兴趣建模，推理时无需大模型
LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation
- 结合用户行为与语义信息双视角建模多兴趣
- 在四个数据集上显著提升推荐准确率
- 推理阶段不依赖大模型，兼顾效果与效率
序列推荐（SR）通过捕捉用户动态偏好提升推荐性能，近年多兴趣学习方法被引入以建模多样化兴趣。然而，多数模型依赖噪声大、稀疏的隐式反馈，限制了推荐精度。大型语言模型（LLMs）虽能处理低质量数据，但其高计算成本和延迟阻碍了在序列推荐中的应用。本文提出一种基于大模型的双层多兴趣学习框架，融合隐式行为与显式语义双重视角。框架包含两个模块：隐式行为兴趣模块（IBIM）通过传统序列推荐模型从用户行为中学习；显式语义兴趣模块（ESIM）利用聚类与提示工程的大模型，从高质量样本中提取语义多兴趣表示。通过模态对齐与语义预测任务，ESIM提供的语义洞察增强IBIM的行为表示。推理阶段仅使用IBIM，实现无大模型的高效推荐。在四个真实数据集上的实验验证了该框架的有效性与实用性。
原文摘要 · Abstract (English)
Sequential recommendation (SR) leverages users' dynamic preferences, with recent advances incorporating multi-interest learning to model diverse user interests. However, most multi-interest SR models rely on noisy, sparse implicit feedback, limiting recommendation accuracy. Large language models (LLMs) offer robust reasoning on low-quality data but face high computational costs and latency challenges for SR integration. We propose a novel LLM-based multi-interest SR framework combining implicit behavioral and explicit semantic perspectives. It includes two modules: the Implicit Behavioral Interest Module (IBIM), which learns from user behavior using a traditional SR model, and the Explicit Semantic Interest Module (ESIM), which uses clustering and prompt-engineered LLMs to extract semantic multi-interest representations from informative samples. Semantic insights from ESIM enhance IBIM's behavioral representations via modality alignment and semantic prediction tasks. During inference, only IBIM is used, ensuring efficient, LLM-free recommendations. Experiments on four real-world datasets validate the framework's effectiveness and practicality.
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