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.

翻译：推荐系统已成为电子商务和社交媒体平台等各类应用的核心基石。在数字时代，推荐系统的发展至关重要，它能够根据用户偏好定制个性化体验。大型语言模型催生了一种新范式——生成式检索与推荐。尽管潜力巨大，生成式推荐方法仍面临幻觉问题（导致推荐性能下降）以及实际场景中高昂的计算成本等挑战。为解决这些问题，我们提出了HGLMRec，一种基于多大型语言模型智能体的新型推荐系统，其引入的超图编码器旨在捕捉用户与物品之间复杂的多行为关联。HGLMRec模型在推理过程中仅检索相关标记，在丰富检索上下文的同时降低了计算开销。实验结果表明，HGLMRec以较低计算成本实现了优于当前最先进基线的性能提升。