Recommendation in an Evolving Service Ecosystem Based on Network Prediction
Recommendation in an Evolving Service Ecosystem Based on Network Prediction
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DOI：
10.1109/tase.2013.2297026
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发表时间：
2014-01
影响因子：
5.6
通讯作者：
Keman Huang;Yushun Fan;Wei Tan
中科院分区：
文献类型：
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作者：
Keman Huang;Yushun Fan;Wei Tan
服务计算在业务自动化中起着至关重要的作用，我们可以观察到当今Web服务及其组合的快速增长。Web服务、它们的组合、提供者、消费者和其他实体（如上下文信息）共同构成了一个不断发展的服务生态系统。已经提出了许多服务推荐方法来促进服务的使用。然而，现有的方法大多基于使用模式的所有时间统计，并且忽略了时间方面，即，生态系统的进化。因此，建议可能包含过时的服务，也不能反映生态系统中的最新趋势。为了克服这一局限性，我们提出了一个创新的三阶段网络预测方法（NPA）的进化感知推荐。首先，我们引入了一个网络系列模型来形式化的服务生态系统的演变，然后开发了一个网络分析方法来研究的使用模式，特别侧重于其时间演变。然后提出了一种基于秩聚合的服务网络预测方法来预测网络的演化。最后，使用网络预测模型，我们提出了如何推荐潜在的组合，顶级服务和服务链，分别。在真实的ProgrammableWeb数据集上的实验表明，与那些对服务生态系统演化不可知的方法相比，该方法在服务推荐方面具有上级性能.
Service computing plays a critical role in business automation and we can observe a rapid increase of web services and their compositions nowadays. Web services, their compositions, providers, consumers, and other entities such as context information, collectively form an evolving service ecosystem. Many service recommendation methods have been proposed to facilitate the use of services. However, existing approaches are mostly based on all-time statistics of usage patterns, and overlook the temporal aspect, i.e., the evolution of the ecosystem. As a result, recommendation may consist of obsolete services and also does not reflect the latest trend in the ecosystem. In order to overcome this limitation, we propose an innovative three-phase network prediction approach (NPA) for evolution-aware recommendation. First, we introduce a network series model to formalize the evolution of the service ecosystem and then develop a network analysis method to study the usage pattern with a special focus on its temporal evolution. Afterward a novel service network prediction method based on rank aggregation is proposed to predict the evolution of the network. Finally, using the network prediction model, we present how to recommend potential compositions, top services and service chains, respectively. Experiments on the real-world ProgrammableWeb data set show that our method achieves a superior performance in service recommendation, compared with those that are agnostic to the evolution of a service ecosystem.