Service recommendation for mashup composition with implicit correlation regularization

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

doi:10.1109/ICWS.2015.38

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 proceeding › Conference proceeding contribution › peer-review

46 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 language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	217-224
Number of pages	8
ISBN (Electronic)	9781467380904
DOIs	https://doi.org/10.1109/ICWS.2015.38
Publication status	Published - 13 Aug 2015
Externally published	Yes
Event	IEEE International Conference on Web Services, ICWS 2015 - New York, United States Duration: 27 Jun 2015 → 2 Jul 2015

Other

Other	IEEE International Conference on Web Services, ICWS 2015
Country/Territory	United States
City	New York
Period	27/06/15 → 2/07/15

Keywords

latent variable model
mashup
matrix factorization
recommendation

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10.1109/ICWS.2015.38

Cite this

@inproceedings{e407655308f74f0fab403009d692bafc,

title = "Service recommendation for mashup composition with implicit correlation regularization",

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.",

keywords = "latent variable model, mashup, matrix factorization, recommendation",

author = "Lina Yao and Xianzhi Wang and Sheng, {Quan Z.} and Wenjie Ruan and Wei Zhang",

year = "2015",

month = aug,

day = "13",

doi = "10.1109/ICWS.2015.38",

language = "English",

pages = "217--224",

booktitle = "Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "IEEE International Conference on Web Services, ICWS 2015 ; Conference date: 27-06-2015 Through 02-07-2015",

}

Yao, L, Wang, X, Sheng, QZ, Ruan, W & Zhang, W 2015, Service recommendation for mashup composition with implicit correlation regularization. in Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 217-224, IEEE International Conference on Web Services, ICWS 2015, New York, United States, 27/06/15. https://doi.org/10.1109/ICWS.2015.38

Service recommendation for mashup composition with implicit correlation regularization. / Yao, Lina; Wang, Xianzhi; Sheng, Quan Z. et al.
Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 217-224.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Service recommendation for mashup composition with implicit correlation regularization

AU - Yao, Lina

AU - Wang, Xianzhi

AU - Sheng, Quan Z.

AU - Ruan, Wenjie

AU - Zhang, Wei

PY - 2015/8/13

Y1 - 2015/8/13

N2 - 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.

AB - 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.

KW - latent variable model

KW - mashup

KW - matrix factorization

KW - recommendation

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DO - 10.1109/ICWS.2015.38

M3 - Conference proceeding contribution

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EP - 224

BT - Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - IEEE International Conference on Web Services, ICWS 2015

Y2 - 27 June 2015 through 2 July 2015

ER -

Service recommendation for mashup composition with implicit correlation regularization

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