Home > Research > Publications & Outputs > Service Recommendation for Mashup Composition w...

Links

Text available via DOI:

View graph of relations

Service Recommendation for Mashup Composition with Implicit Correlation Regularization

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Close
NullPointerException

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.