用知识图谱增强语言代理，让推荐更懂用户偏好
Knowledge Graph Enhanced Language Agents for Recommendation
- 将知识图谱路径转为自然语言，融入语言代理交互
- 在三个数据集上NDCG@1提升33%至95%
- 适合做可解释推荐与智能代理系统的研究者
语言代理最近被用于模拟人类行为和用户-物品交互以改进推荐系统。然而，现有方法缺乏对用户与物品间关系的理解，导致用户画像不准确，推荐效果不佳。本文探索利用知识图谱（KG）中丰富的用户-物品关系来提升推荐性能。核心洞察是：知识图谱中的路径可捕捉用户与物品间的复杂关联，揭示用户偏好的深层原因，从而丰富用户画像。为此，我们提出知识图谱增强型语言代理（KGLA）框架，统一语言代理与知识图谱。在模拟推荐场景中，将用户和物品置于知识图谱中，并将图谱路径转化为自然语言描述融入模拟过程。这使语言代理能基于充分理由进行交互，提升模拟真实性，进而改善推荐表现。实验表明，相较于最先进基线方法，KGLA在三个主流基准上实现NDCG@1提升33%至95%。
原文摘要 · Abstract (English)
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.
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