Service Recommendation for Mashup Composition with Implicit Correlation Regularization

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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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. ed. / Hong Zhu; John A. Miller. IEEE, 2015. p. 217-224 7195572.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Yao, L, Wang, X, Sheng, QZ, Ruan, W & Zhang, W 2015, Service Recommendation for Mashup Composition with Implicit Correlation Regularization. in H Zhu & JA Miller (eds), Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015., 7195572, IEEE, 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

APA

Yao, L., Wang, X., Sheng, Q. Z., Ruan, W., & Zhang, W. (2015). Service Recommendation for Mashup Composition with Implicit Correlation Regularization. In H. Zhu, & J. A. Miller (Eds.), Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015 (pp. 217-224). Article 7195572 IEEE. https://doi.org/10.1109/ICWS.2015.38

Vancouver

Yao L, Wang X, Sheng QZ, Ruan W, Zhang W. Service Recommendation for Mashup Composition with Implicit Correlation Regularization. In Zhu H, Miller JA, editors, Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015. IEEE. 2015. p. 217-224. 7195572 doi: 10.1109/ICWS.2015.38

Author

Yao, Lina ; Wang, Xianzhi ; Sheng, Quan Z. et al. / Service Recommendation for Mashup Composition with Implicit Correlation Regularization. Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015. editor / Hong Zhu ; John A. Miller. IEEE, 2015. pp. 217-224

Bibtex

@inproceedings{3f9d72309ac740bd96545bb9623d8994,

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",

editor = "Hong Zhu and Miller, {John A.}",

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

publisher = "IEEE",

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

}

RIS

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

U2 - 10.1109/ICWS.2015.38

DO - 10.1109/ICWS.2015.38

M3 - Conference contribution/Paper

AN - SCOPUS:84956622228

SP - 217

EP - 224

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

A2 - Zhu, Hong

A2 - Miller, John A.

PB - IEEE

T2 - IEEE International Conference on Web Services, ICWS 2015

Y2 - 27 June 2015 through 2 July 2015

ER -

Research

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