2024-10-25
arXiv

Knowledge Graph Enhanced Language Agents for Recommendation

Taicheng Guo , Xiangliang Zhang , Chaochun Liu , Hai Wang , Varun Mannam
This paper introduces Knowledge Graph Enhanced Language Agents (KGLA), a framework that integrates knowledge graphs with language agents to improve recommendation systems by enriching user profiles and capturing complex relationships between users and items. The method significantly enhances recommendation performance, as demonstrated by substantial improvements in NDCG@1 on three widely used benchmarks.
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.