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Service Recommendation for Mashup Composition with Implicit Correlation Regularization

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Publication date13/08/2015
Host publicationProceedings - 2015 IEEE International Conference on Web Services, ICWS 2015
EditorsHong Zhu, John A. Miller
PublisherIEEE
Pages217-224
Number of pages8
ISBN (Electronic)9781467372725
<mark>Original language</mark>English
EventIEEE International Conference on Web Services, ICWS 2015 - New York, United States
Duration: 27/06/20152/07/2015

Conference

ConferenceIEEE International Conference on Web Services, ICWS 2015
Country/TerritoryUnited States
CityNew York
Period27/06/152/07/15

Conference

ConferenceIEEE International Conference on Web Services, ICWS 2015
Country/TerritoryUnited States
CityNew York
Period27/06/152/07/15

Abstract

In 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 co-invocation 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 Programmable Web, and extensively evaluated the effectiveness of our proposed approach.