Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/110032
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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 - 2 Jul 2015 : New York, NY) |
Editor: | Miller, J. |
Statement of Responsibility: | 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 |
DOI: | 10.1109/ICWS.2015.38 |
Published version: | http://dx.doi.org/10.1109/icws.2015.38 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
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RA_hdl_110032.pdf | Restricted Access | 1.13 MB | Adobe PDF | View/Open |
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