Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs

Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie
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引用次数: 79

Abstract

Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization problem. Traditional recommendation methods usually adopt brute-force methods to find feasible paths, which results in issues related to convergence and explainability. In this paper, we address these issues by better supervising the path finding process. The key idea is to extract imperfect path demonstrations with minimum labeling efforts and effectively leverage these demonstrations to guide path finding. In particular, we design a demonstration-based knowledge graph reasoning framework for explainable recommendation. We also propose an ADversarial Actor-Critic (ADAC) model for the demonstration-guided path finding. Experiments on three real-world benchmarks show that our method converges more quickly than the state-of-the-art baseline and achieves better recommendation accuracy and explainability.
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利用知识图上的强化推荐推理演示
知识图谱被广泛用于提高推荐的准确性。知识图上的多跳用户-项目连接也赋予了为什么推荐一个项目的推理。然而,路径推理是一个复杂的组合优化问题。传统的推荐方法通常采用蛮力方法寻找可行路径,这导致了收敛性和可解释性问题。在本文中,我们通过更好地监督寻路过程来解决这些问题。关键思想是用最小的标记工作提取不完美的路径演示,并有效地利用这些演示来指导寻路。特别地,我们为可解释推荐设计了一个基于演示的知识图推理框架。我们还提出了一个对抗的行动者-评论家(ADAC)模型,用于演示导向的寻路。在三个现实世界基准上的实验表明,我们的方法比最先进的基线收敛得更快,并且获得了更好的推荐准确性和可解释性。
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