计算机科学 ›› 2024, Vol. 51 ›› Issue (11): 103-111.doi: 10.11896/jsjkx.231100010
杨东昇, 王桂玲, 郑鑫
YANG Dongsheng, WANG Guiling, ZHENG Xin
摘要： 随着Internet和Web上各种服务和API数量的迅速增加,开发人员要快速准确地找到满足其需求的API变得越来越具有挑战性,因此亟需一个高效的推荐系统。目前,将图神经网络应用于服务推荐领域取得了巨大成功,但大多数方法仍然局限于简单的交互,忽略了mashup和API调用之间的内在关系;为了解决这个问题,提出了一种基于层次超图注意力的服务推荐方法(H-HGSR)来进行API推荐。首先定义了8种类型的超边,并探究了对应类型超边的超图邻接矩阵生成方法,然后提出了节点级和超边级的注意力机制。节点级注意力机制用于聚合特定类型超图邻接矩阵下的不同邻居的重要信息,以捕获mashup和API之间的高阶关系;超边级注意力机制用于对从不同类型超图邻接矩阵生成的节点嵌入进行加权组合。通过学习节点级和超边级注意力的重要性,可以获得更准确的嵌入表示。最后使用一个多层感知器神经网络(MLP)进行服务推荐。在Programmable Web真实数据集上进行了大量实验,结果表明,所提H-HGSR框架优于目前最先进的服务推荐方法。
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