Web services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component services from a sea of service candidates is often challenging. Therefore, web service recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by service providers. In this paper, we propose a novel way of dynamically reconstructing objective service profiles based on mashup descriptions, which carry historical information of how services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of services and bridge the vocabulary gap between mashup developers and service providers. Specifically, we jointly model mashup descriptions and component service using author topic model in order to reconstruct service profiles. Exploiting word features derived from the reconstructed service profiles, a new service recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed service recommendation algorithm is effective and outperforms the state-of-the-art methods.Note to Practitioners—Service recommendation accuracy for mashup creation is often limited due to poor quality of service descriptions. Mashup descriptions contain valuable information about functions and features of its component services, which can be leveraged to enhance descriptive quality of original service profiles. Based on the assumption, this paper proposes a novel two-phase service recommendation framework to facilitate mashup creation. Specifically, our approach reconstructs service profiles by extracting appropriate words from historical mashup descriptions. Then, a novel service recommendation algorithm is developed by exploiting popularity and relevance measures hidden in the reconstructed profiles. Moreover, we propose the rules of dominant words discovery and employ it to further refine our algorithm.

中文翻译：

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从混搭描述重构了配置文件的Web服务建议

Web服务是支持Internet上业务流程自动化的独立软件组件，而mashup是一种流行的技术，可创建增值服务组合来满足复杂的业务需求。对于mashup开发人员而言，从大量的服务候选者中寻找所需的组件服务通常具有挑战性。因此，Web服务推荐已成为要求很高的技术。但是，传统方法主要依赖于服务提供商提供的静态且可能在主观上描述的文本。在本文中，我们提出了一种新的基于mashup描述的动态重建目标服务配置文件的方法，该描述携带了如何在mashup中使用服务的历史信息。我们的关键思想是利用mashup的描述和结构来发现服务的重要单词特征，并弥合mashup开发人员和服务提供商之间的词汇鸿沟。具体来说，我们使用作者主题模型对混搭描述和组件服务进行联合建模，以重建服务配置文件。利用从重构的服务配置文件中导出的单词特征，开发了一种新的服务推荐算法。对来自ProgrammableWeb.com的真实数据集的实验表明，我们提出的服务推荐算法是有效的，并且优于最新方法。服务说明。混搭描述包含有关其组件服务的功能和特性的有价值的信息，可以利用这些信息来增强原始服务配置文件的描述性质量。基于此假设，本文提出了一种新颖的两阶段服务推荐框架，以促进混搭创建。具体来说，我们的方法通过从历史混搭描述中提取适当的单词来重建服务配置文件。然后，通过利用隐藏在重构配置文件中的受欢迎程度和相关性度量来开发一种新颖的服务推荐算法。此外，我们提出了优势词发现规则，并将其用于进一步完善我们的算法。本文提出了一种新颖的两阶段服务推荐框架，以促进mashup的创建。具体来说，我们的方法通过从历史混搭描述中提取适当的单词来重建服务配置文件。然后，通过利用隐藏在重构配置文件中的受欢迎程度和相关性度量来开发一种新颖的服务推荐算法。此外，我们提出了优势词发现规则，并将其用于进一步完善我们的算法。本文提出了一种新颖的两阶段服务推荐框架，以促进mashup的创建。具体来说，我们的方法通过从历史混搭描述中提取适当的单词来重建服务配置文件。然后，通过利用隐藏在重构配置文件中的受欢迎程度和相关性度量来开发一种新颖的服务推荐算法。此外，我们提出了优势词发现规则，并将其用于进一步完善我们的算法。