Service recommendation for mashup composition with implicit correlation regularization

Lina Yao, Xianzhi Wang, Quan Z. Sheng, Wenjie Ruan, Wei Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

28 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Web Services, ICWS 2015
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages217-224
Number of pages8
ISBN (Electronic)9781467380904
DOIs
Publication statusPublished - 13 Aug 2015
Externally publishedYes
EventIEEE International Conference on Web Services, ICWS 2015 - New York, United States
Duration: 27 Jun 20152 Jul 2015

Other

OtherIEEE International Conference on Web Services, ICWS 2015
CountryUnited States
CityNew York
Period27/06/152/07/15

Keywords

  • latent variable model
  • mashup
  • matrix factorization
  • recommendation

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