QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications

Cao, B; Liu, J; Wen, Y; Li, H; Xiao, Q; Chen, J

QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications

Cao, B Liu, J Wen, Y Li, H Xiao, Q Chen, J

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Parallel and Distributed Computing, 2019, 132, pp. 177-189
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Cao, B
dc.contributor.author	Liu, J
dc.contributor.author	Wen, Y
dc.contributor.author	Li, H
dc.contributor.author	Xiao, Q
dc.contributor.author	Chen, J
dc.date.accessioned	2020-05-11T06:06:19Z
dc.date.available	2020-05-11T06:06:19Z
dc.date.issued	2019
dc.identifier.citation	Journal of Parallel and Distributed Computing, 2019, 132, pp. 177-189
dc.identifier.issn	0743-7315
dc.identifier.issn	1096-0848
dc.identifier.uri	http://hdl.handle.net/10453/140632
dc.description.abstract	© 2018 Elsevier Inc. IoT Mashup applications allow developer to compose existing Web APIs to create value-added composite Web services. The rapid growth of large-scale and complex services makes it difficult to find suitable Web APIs to build IoT Mashup applications for developers. Even if the existing service recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and multiple-dimension information of QoS between Mashup and services on recommendation accuracy. In this paper, we propose a QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. This method first uses relational topic model to characterize the relationships among Mashup, services, and their links, and mine the latent topics derived by the relationships. Second, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and services to recommend adequate relevant top-k Web APIs for target IoT Mashup creation. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, and F-measure.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Parallel and Distributed Computing
dc.relation.isbasedon	10.1016/j.jpdc.2018.04.002
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications
dc.type	Journal Article
utslib.citation.volume	132
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-11T06:06:17Z
pubs.publication-status	Accepted
pubs.volume	132
utslib.start-page	177

Abstract:

© 2018 Elsevier Inc. IoT Mashup applications allow developer to compose existing Web APIs to create value-added composite Web services. The rapid growth of large-scale and complex services makes it difficult to find suitable Web APIs to build IoT Mashup applications for developers. Even if the existing service recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and multiple-dimension information of QoS between Mashup and services on recommendation accuracy. In this paper, we propose a QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. This method first uses relational topic model to characterize the relationships among Mashup, services, and their links, and mine the latent topics derived by the relationships. Second, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and services to recommend adequate relevant top-k Web APIs for target IoT Mashup creation. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, and F-measure.

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http://hdl.handle.net/10453/140632