Abstract
Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the relationships between users and items, leading to inaccurate user profiles and ineffective recommendations. In this work, we explore the utility of Knowledge Graphs (KGs), which contain extensive and reliable relationships between users and items, for recommendation. Our key insight is that the paths in a KG can capture complex relationships between users and items, eliciting the underlying reasons for user preferences and enriching user profiles. Leveraging this insight, we propose Knowledge Graph Enhanced Language Agents(KGLA), a framework that unifies language agents and KG for recommendation systems. In the simulated recommendation scenario, we position the user and item within the KG and integrate KG paths as natural language descriptions into the simulation. This allows language agents to interact with each other and discover sufficient rationale behind their interactions, making the simulation more accurate and aligned with real-world cases, thus improving recommendation performance. Our experimental results show that KGLA significantly improves recommendation performance (with a 33%-95% boost in NDCG@1 among three widely used benchmarks) compared to the previous best baseline method.
Abstract (translated)
最近，语言代理被用于模拟推荐系统中的人类行为和用户-项目互动。然而，当前的语言代理仿真未能理解用户与项目之间的关系，导致用户画像不准确且推荐效果不佳。在此研究中，我们探索了知识图谱（KGs）在推荐中的效用，这些知识图谱包含广泛且可靠地描述用户与项目之间关系的信息。我们的关键见解是，知识图谱中的路径能够捕捉到用户与项目之间的复杂关系，揭示用户偏好的潜在原因，并丰富用户画像。基于这一洞见，我们提出了Knowledge Graph Enhanced Language Agents (KGLA)，一个统一语言代理和KG用于推荐系统的框架。在模拟推荐场景中，我们将用户和项目置于知识图谱内，并将KG路径作为自然语言描述整合到仿真中。这使得语言代理能够彼此互动并发现其互动背后的充分理由，从而使仿真更准确，更贴近现实情况，进而提高推荐性能。我们的实验结果显示，与之前的最佳基准方法相比，KGLA显著提高了推荐性能（在三个广泛使用的基准上，NDCG@1的提升幅度为33%-95%）。
URL
https://arxiv.org/abs/2410.19627
