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Type: Conference paper
Title: Service recommendation for mashup composition with implicit correlation regularization
Author: Yao, L.
Wang, X.
Sheng, Q.
Ruan, W.
Zhang, W.
Citation: Proceedings 2015 IEEE International Conference on Web Services, 2015 / Miller, J. (ed./s), pp.217-224
Publisher: IEEE
Issue Date: 2015
ISBN: 9781467380904
Conference Name: International Conference on Web Services (ICWS) (27 Jun 2015 - 02 Jul 2015 : New York, NY)
Statement of
Lina Yao, Xianzhi Wang, Quan Z. Sheng, Wenjie Ruan, and Wei Zhang
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 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.
Keywords: Recommendation; matrix factorization; mashup; latent variable model
Rights: © 2015 IEEE
RMID: 0030043808
DOI: 10.1109/ICWS.2015.38
Appears in Collections:Computer Science publications

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