所有节点都能从知识图谱中同等受益吗？面向推荐的自适应节点感知知识图谱融合
Do All Nodes Benefit Equally from Knowledge Graphs? Adaptive Node-Aware KG Fusion for Recommendation
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中文总结 AI 辅助
本文提出AdaKG，一种自适应节点感知知识图谱融合方法，通过分别编码交互图和知识图谱，并基于协同过滤信号稳定性动态调整各节点对知识图谱的依赖程度，以提升推荐性能。
中文摘要 AI 辅助
基于知识图谱（KG）的推荐已被广泛研究，通过使用知识图谱来缓解数据稀疏性问题，知识图谱将物品、实体及其关系表示为图，并提供物品侧知识。然而，现有方法在融入物品知识时，并未考虑每个用户或物品节点应多大程度依赖这些知识。因此，它们不加区分地将知识图谱信号应用于所有节点，甚至包括那些来自交互图（IG）的协同过滤（CF）信号已经可靠的节点。在本文中，我们提出AdaKG（自适应节点感知知识图谱融合），一种新颖的基于知识图谱的推荐方法，能够自适应地调整辅助知识对每个节点的贡献。由于用户-物品交互和物品知识提供不同类型的信号，直接混合它们可能会扭曲协同过滤信号。为避免这一问题，AdaKG使用视图特定编码器分别对交互图和知识图谱进行编码，使每个视图能够捕获自身的信息。然后，通过在小幅对抗扰动下衡量其协同过滤信号的稳定性，来估计每个节点应多大程度依赖物品知识，为较不稳定的节点分配更大的知识图谱贡献。最后，AdaKG在共享空间中对交互图和知识图谱的嵌入进行自适应对齐，并根据估计的节点级依赖度进行融合。通过实验，我们表明AdaKG相比基线方法取得了强劲的性能，并验证了我们的自适应融合策略的有效性。
英文摘要
KG-aware recommendation has been widely studied to alleviate data sparsity by using knowledge graphs (KGs), which represent items, entities, and their relations as graphs and provide item-side knowledge. However, existing methods incorporate item knowledge without considering how much each user or item node should rely on it. As a result, they apply KG signals indiscriminately across nodes, even to nodes whose collaborative filtering (CF) signals from the interaction graph (IG) are already reliable. In this paper, we propose AdaKG (Adaptive Node-Aware KG Fusion), a novel KG-aware recommendation method that adaptively adjusts the contribution of auxiliary knowledge for each node. Since user-item interactions and item knowledge provide different types of signals, directly mixing them can distort the CF signals. To avoid this, AdaKG separately encodes the IG and KG with view-specific encoders, allowing each view to capture its own information. It then estimates how strongly each node should rely on item knowledge by measuring the stability of its CF signals under small adversarial perturbations, assigning a larger KG contribution to less stable nodes. Finally, AdaKG adaptively aligns the IG and KG embeddings in a shared space and fuses them according to the estimated node-wise reliance. Through experiments, we show that AdaKG achieves strong performance compared with its baselines and the effectiveness of our adaptive fusion strategy.
发表机构
- Soongsil University（崇实大学）
机构由 AI 辅助整理，请以论文原文为准。
