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