Service Recommendation for Mashup Composition with Implicit Correlation Regularization

Yao, Lina and Wang, Xianzhi and Sheng, Quan Z. and Ruan, Wenjie and Zhang, Wei (2015) Service Recommendation for Mashup Composition with Implicit Correlation Regularization. In: Proceedings - 2015 IEEE International Conference on Web Services, ICWS 2015. IEEE, USA, pp. 217-224. ISBN 9781467372725

Full text not available from this repository.


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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
Deposited By:
Deposited On:
22 Jun 2019 00:59
Last Modified:
21 Nov 2022 16:54