用超图与多大模型代理提升推荐系统效率与准确性
Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems
- 构建多大模型代理+超图编码器，捕捉用户与物品的复杂行为关系
- 推理时仅检索相关词元，计算成本降低且推荐效果优于现有方法
- 适合关注生成式推荐系统效率优化的研究者与工程师
推荐系统已成为电商和社交媒体平台的核心。在数字时代，个性化体验至关重要。大语言模型（LLMs）推动了生成式检索与推荐新范式，但存在幻觉问题导致性能下降，且实际应用中计算成本高。为此，我们提出HGLMRec，一种基于多大模型代理的推荐系统，引入超图编码器以捕捉用户与物品间的复杂多行为关系。该模型在推理时仅检索相关词元，降低计算开销的同时丰富检索上下文。实验表明，相比当前最优基线，HGLMRec在更低计算成本下实现性能提升。
原文摘要 · Abstract (English)
Recommender Systems (RSs) have become the cornerstone of various applications such as e-commerce and social media platforms. The evolution of RSs is paramount in the digital era, in which personalised user experience is tailored to the user's preferences. Large Language Models (LLMs) have sparked a new paradigm - generative retrieval and recommendation. Despite their potential, generative RS methods face issues such as hallucination, which degrades the recommendation performance, and high computational cost in practical scenarios. To address these issues, we introduce HGLMRec, a novel Multi-LLM agent-based RS that incorporates a hypergraph encoder designed to capture complex, multi-behaviour relationships between users and items. The HGLMRec model retrieves only the relevant tokens during inference, reducing computational overhead while enriching the retrieval context. Experimental results show performance improvement by HGLMRec against state-of-the-art baselines at lower computational cost.
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