Extracting Relevant Terms from Mashup Descriptions for Service Recommendation
Extracting Relevant Terms from Mashup Descriptions for Service Recommendation作者机构:Department of AutomationTsinghua UniversityBeijing 100084China
出 版 物:《Tsinghua Science and Technology》 (清华大学学报(自然科学版(英文版))
年 卷 期:2017年第22卷第3期
页 面:293-302页
核心收录:
学科分类:08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术]
基 金:supported in part by the National Natural Science Foundation of China(No.61673230)
主 题:service recommendation topic model mashup descriptions linear discriminant function
摘 要: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 *** show that the proposed model significantly outperforms state-of-the-art methods.