计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 64-69.doi: 10.11896/jsjkx.210600111
秦琪琦, 张月琴, 王润泽, 张泽华
QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua
摘要： 基于图神经网络的推荐系统是当前数据挖掘应用的研究热点。在异质信息网络(Heterogeneous Information Network,HIN)上结合图神经网络进行推荐,可通过用户的关联信息来学习用户的偏好,从而提升推荐性能。但现有基于HIN的推荐方法大多存在不能有效地解释高阶建模结果及人工设计元路径需要相关领域知识的问题。因此,结合层次粒化思想,在异质推荐过程中引入知识图谱,提出一种基于知识图谱的异质推荐方法(Heterogeneous Recommendation Methods for Knowledge Graphs,HKR)。该方法首先结合知识图谱,对局部上下文和非局部上下文进行层次粒化,分别学习用户特征的粗粒度表示;然后基于门控机制结合局部和非局部的属性节点嵌入,进一步学习用户和项目之间的潜在特征;最后将细粒度的特征融合用于推荐。在真实的大规模数据集上的实验结果表明,所提方法的性能在多方面评测上均优于目前的基于知识图谱的图神经网络推荐方法。
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