CSBR: A Compositional Semantics-Based Service Bundle Recommendation Approach for Mashup Development
Qi Gu, Jian Cao, Yancen Liu
Abstract
An increasing number of services are being offered which leads to difficulties in choosing appropriate services during mashup development. Currently, several service recommendation techniques have been developed for mashup creation, however, they are largely limited to suggesting services which have similar functionalities. The fundamental problem with these techniques is that they do not consider the large semantic gap between mashup descriptions and service descriptions. In this article, we propose a compositional semantics-based service bundle recommendation model (CSBR) to tackle this problem. CSBR is based on a semantic service package repository, which is constructed by mining the existing mashups. Specifically, the reusable service packages, which consist of multiple collaborative services, are annotated with composite semantics rather than their original semantics. Based on the semantic service package repository, CSBR can recommend a bundle of services that cover the functional requirements of the mashup as completely as possible. Extensive experiments are conducted on a real-world dataset and the results show CSBR achieves significant performance improvements in both precision and recall metrics over the state-of-the-art methods.