Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
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DOI：
10.1145/3397271.3401040
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Chong Chen;Min Zhang;Weizhi Ma;Yiqun Liu;Shaoping Ma
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文献类型：
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作者：
Chong Chen;Min Zhang;Weizhi Ma;Yiqun Liu;Shaoping Ma
知识图谱（KG）包含结构良好的外部信息，并已被证明是有效的高质量的推荐。然而，现有的KG增强推荐方法主要集中在探索先进的神经网络架构，以更好地调查KG的结构信息。而对于模型学习，这些方法主要依赖于负采样（NS）来优化KG嵌入任务和推荐任务的模型。由于NS不是鲁棒的（例如，对一小部分负面实例进行采样可能会丢失大量有用的信息），因此有理由认为这些方法不足以捕获用户、项目和实体之间的协作信息。在本文中，我们提出了一种新的知识图增强推荐（JNSKR）的联合非采样学习模型。具体来说，我们首先设计了一个新的高效NS优化算法的知识图嵌入学习。然后，子图由所提出的注意神经网络编码，以更好地表征用户对项目的偏好。通过新颖的记忆策略和联合学习框架的设计，JNSKR不仅可以对用户、项目和实体之间的细粒度连接进行建模，而且可以从整个训练数据（包括所有未观察到的数据）中有效地学习模型参数，时间复杂度相当低。在两个公共基准测试上的实验结果表明，JNSKR的性能明显优于RippleNet和KGAT等最先进的方法。值得注意的是，JNSKR在训练效率上也表现出显著优势（比KGAT快约20倍），这使得它更适用于现实世界的大规模系统。
Knowledge graph (KG) contains well-structured external information and has shown to be effective for high-quality recommendation. However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG. While for model learning, these methods mainly rely on Negative Sampling (NS) to optimize the models for both KG embedding task and recommendation task. Since NS is not robust (e.g., sampling a small fraction of negative instances may lose lots of useful information), it is reasonable to argue that these methods are insufficient to capture collaborative information among users, items, and entities. In this paper, we propose a novel Jointly Non-Sampling learning model for Knowledge graph enhanced Recommendation (JNSKR). Specifically, we first design a new efficient NS optimization algorithm for knowledge graph embedding learning. The subgraphs are then encoded by the proposed attentive neural network to better characterize user preference over items. Through novel designs of memorization strategies and joint learning framework, JNSKR not only models the fine-grained connections among users, items, and entities, but also efficiently learns model parameters from the whole training data (including all non-observed data) with a rather low time complexity. Experimental results on two public benchmarks show that JNSKR significantly outperforms the state-of-the-art methods like RippleNet and KGAT. Remarkably, JNSKR also shows significant advantages in training efficiency (about 20 times faster than KGAT), which makes it more applicable to real-world large-scale systems.