Topology and Topic-Aware Service Clustering

Weifeng Pan, Jilei Dong, Kun Liu and Jing Wang
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Weifeng Pan: Zhejiang Gongshang University, Hangzhou, China
Jilei Dong: University of Connecticut, Storrs, USA
Kun Liu: Hubei University of Economics, Wuhan, China
Jing Wang: Jiangxi University of Finance and Economics, Nanchang, China

International Journal of Web Services Research (IJWSR), 2018, vol. 15, issue 3, 18-37

Abstract: This article describes how the number of services and their types being so numerous makes accurately discovering desired services become a problem. Service clustering is an effective way to facilitate service discovery. However, the existing approaches are usually designed for a single type of service documents, neglecting to fully use the topic and topological information in service profiles and usage histories. To avoid these limitations, this article presents a novel service clustering approach. It adopts a bipartite network to describe the topological structure of service usage histories and uses a SimRank algorithm to measure the topological similarity of services; It applies Latent Dirichlet Allocation to extract topics from service profiles and further quantifies the topic similarity of services; It quantifies the similarity of services by integrating topological and topic similarities; It uses the Chameleon clustering algorithm to cluster the services. The empirical evaluation on real-world data set highlights the benefits provided by the combination of topological and topic similarities.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:15:y:2018:i:3:p:18-37

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