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Knowledge graph enhanced neural collaborative recommendation
Expert Systems with Applications ( IF 8.5 ) Pub Date : 2020-09-12 , DOI: 10.1016/j.eswa.2020.113992
Lei Sang , Min Xu , Shengsheng Qian , Xindong Wu

Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions.

To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users’ potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework.



中文翻译:

知识图增强神经协作推荐

现有的神经协作过滤(NCF)推荐方法存在严重的稀疏性问题。知识图谱(KG)通常由关于项目的卓有成效的关联事实组成,为缓解稀疏性问题提供了前所未有的机会。但是,纯NCF模型几乎无法对KG中的高阶连通性进行建模,而忽略了用户/项嵌入维度之间的复杂成对相关性。

为了解决这些问题,我们提出了一种新颖的知识图增强神经协作推荐(K-NCR)框架,该框架将推荐任务的用户-项目交互信息和辅助知识信息有效地组合为三个部分:(1)对于项目,建议的传播模型学习项目实体的表示。它以递归方式聚合来自KG中多跳邻居的信息,并采用一种关注机制来区分关系类型对矿山用户潜在偏好的重要性。(2)对于用户,利用另一种异构注意力权重来加强用户的嵌入学习。(3)然后,将用户和项目嵌入嵌入到新设计的具有卷积隐藏层的二维交互映射中,以明确地模拟其嵌入维度之间的复杂成对相关性。在三个基准数据集上的大量实验结果证明了我们的K-NCR框架的有效性。

更新日期:2020-09-12
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