用预训练的用户行为嵌入增强大模型，提升推荐准确率。
Break the ID-Language Barrier: An Adaption Framework for LLM-based Sequential Recommendation
- 将用户行为嵌入融入大模型，生成更丰富的推荐表示
- 在多个数据集上提升命中率超10%，NDCG超20%
- 适用于各类序列推荐模型与大模型组合
大语言模型在自然语言处理中的突破引发了推荐系统领域的探索，但其缺乏领域特定知识仍是关键瓶颈。特别是，在序列推荐中，大模型缺少用户行为模式等关键信息。为此，本文提出IDLE-Adapter框架，通过整合富含领域知识的预训练ID嵌入，提升大模型的推荐性能。该框架通过预训练的ID序列模型、维度对齐、逐层嵌入优化和逐层分布对齐，将稀疏的用户-物品交互数据转化为稠密、适配大模型的表示。此外，该框架具备高度灵活性，可无缝集成不同来源的基于ID的序列模型与大模型架构。在多个数据集上的大量实验表明，相较于现有最优方法，IDLE-Adapter在HitRate@5上提升超过10%，在NDCG@5上提升超过20%。
原文摘要 · Abstract (English)
The recent breakthrough of large language models (LLMs) in natural language processing has sparked exploration in recommendation systems, however, their limited domain-specific knowledge remains a critical bottleneck. Specifically, LLMs lack key pieces of information crucial for sequential recommendations, such as user behavior patterns. To address this critical gap, we propose IDLE-Adapter, a novel framework that integrates pre-trained ID embeddings, rich in domain-specific knowledge, into LLMs to improve recommendation accuracy. IDLE-Adapter acts as a bridge, transforming sparse user-item interaction data into dense, LLM-compatible representations through a Pre-trained ID Sequential Model, Dimensionality Alignment, Layer-wise Embedding Refinement, and Layer-wise Distribution Alignment. Furthermore, IDLE-Adapter demonstrates remarkable flexibility by seamlessly integrating ID embeddings from diverse ID-based sequential models and LLM architectures. Extensive experiments across various datasets demonstrate the superiority of IDLE-Adapter, achieving over 10\% and 20\% improvements in HitRate@5 and NDCG@5 metrics, respectively, compared to state-of-the-art methods.
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