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dc.contributor.authorYao, L.en
dc.contributor.authorWang, X.en
dc.contributor.authorSheng, Q.en
dc.contributor.authorRuan, W.en
dc.contributor.authorZhang, W.en
dc.identifier.citationProceedings 2015 IEEE International Conference on Web Services, 2015 / Miller, J. (ed./s), pp.217-224en
dc.description.abstractIn this paper, we explore service recommendation and selection in the reusable composition context. The goal is to aid developers finding the most appropriate services in their composition tasks. We specifically focus on mashups, a domain that increasingly targets people without sophisticated programming knowledge. We propose a probabilistic matrix factorization approach with implicit correlation regularization to solve this problem. In particular, we advocate that the coinvocation of services in mashups is driven by both explicit textual similarity and implicit correlation of services, and therefore develop a latent variable model to uncover the latent connections between services by analyzing their co-invocation patterns. We crawled a real dataset from ProgrammableWeb, and extensively evaluated the effectiveness of our proposed approach.en
dc.description.statementofresponsibilityLina Yao, Xianzhi Wang, Quan Z. Sheng, Wenjie Ruan, and Wei Zhangen
dc.rights© 2015 IEEEen
dc.subjectRecommendation; matrix factorization; mashup; latent variable modelen
dc.titleService recommendation for mashup composition with implicit correlation regularizationen
dc.typeConference paperen
dc.contributor.conferenceInternational Conference on Web Services (ICWS) (27 Jun 2015 - 02 Jul 2015 : New York, NY)en
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidZhang, W. [0000-0002-0406-5974]en
Appears in Collections:Computer Science publications

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