Abstract

Recently, mashup developers seek to integrate multiple services with complementary functionalities from a large amount of web services. With so many available web services, it is difficult for developers to choose the right one to develop new mashups. Therefore, it is critical to create and recommend appropriate web services for mashup developers based on their development needs. In the past, various deep models have been proposed to facilitate web service recommendation based on semantic matching of textual descriptions. However, existing deep approaches mainly match global semantic representations while ignoring descriptive structure and tag information. In this paper, we propose a multi-representation web service recommendation model, which simultaneously extracts global, local and tag representations of the description and tag information, respectively. Moreover, we propose a tag-driven attention mechanism to guide the process of information extraction. Experiments over a real-world dataset demonstrate that our proposed service recommendation algorithm can achieve remarkable performance.

中文翻译：

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基于注意力机制的多表示Web服务推荐系统

摘要

最近，混搭开发人员寻求将来自大量 Web 服务的多个服务与互补功能集成起来。可用的 Web 服务如此之多，开发人员很难选择合适的服务来开发新的混搭。因此，根据 mashup 开发人员的开发需求为其创建并推荐合适的 Web 服务至关重要。过去，已经提出了各种深度模型来促进基于文本描述的语义匹配的网络服务推荐。然而，现有的深度方法主要匹配全局语义表示，而忽略了描述结构和标签信息。在本文中，我们提出了一种多表示网络服务推荐模型，该模型同时分别提取描述和标签信息的全局、局部和标签表示。此外，我们提出了一种标签驱动的注意力机制来指导信息提取的过程。对真实世界数据集的实验表明，我们提出的服务推荐算法可以实现卓越的性能。