Extracting Relevant Terms from Mashup Descriptions for Service Recommendation

• Published in: Tsinghua science and technology 2017-06, Vol.22 (3), p.293-302
• Main Authors: Zhong, Yang, Fan, Yushun
• Format: Article
• Language: English
• Subjects: Web服务; 应用程序; 提取; 服务表; 术语; 线性判别函数; 组件服务; 组合技术
• ISSN: 1007-0214; 1878-7606; 1007-0214
• DOI: 10.23919/TST.2017.7914201

Due to the exploding growth in the number of web services, mashup has emerged as a service composition technique to reuse existing services and create new applications with the least amount of effort. Service recommendation is essential to facilitate mashup developers locating desired component services among a large collection of candidates. However, the majority of existing methods utilize service profiles for content matching, not mashup descriptions. This makes them suffer from vocabulary gap and cold-start problem when recommending components for new mashups. In this paper, we propose a two-step approach to generate high-quality service representation from mashup descriptions. The first step employs a linear discriminant function to assign each term with a component service such that a coarse-grained service representation can be derived. In the second step, a novel probabilistic topic model is proposed to extract relevant terms from coarse-grained service representation. Finally, a score function is designed based on the final high-quality representation to determine recommendations. Experiments on a data set from ProgrammableWeb.com show that the proposed model significantly outperforms state-of-the-art methods.