BIGSIR: A Bipartite Graph Based Service Recommendation Method

Abstract

Cloud computing is an Internet-based computing. It relies on sharing computing resources which are delivered as services on the Internet. Web service is one of the most important types of services that can be used in cloud computing. But many of them may be similar in some functional or nonfunctional properties, making how to recommend a suitable web service a problem facing many developers. Researchers have taken the QoS attributes into consideration. However, their research is on the premise that all the recommended web services are compatible, i.e., the recommended web services can be composed with existing web services. It may not always be true. In this paper, we only take the compatibility of web services into consideration, and present a BIpartite Graph based Service Recommendation (BIGSIR) method to address the service compatibility problem. BIGSIR uses the historical usage data of web services to recommend web services to developers. Different from existing web service recommendation approaches, BIGSIR adopts a bipartite graph to visual the web services and the relationship between them. Based on the graph model, an effective recommendation algorithm is introduced to recommend the suitable web services. Our approach is evaluated on a dataset constructed from myExperiment, a search engine that contains about 1, 851 web services and 2, 000 workflows. Experimental results demonstrate that apart from some isolated web services or workflows, BIGSIR can obtain promising results. And we also explore the factors that will influence the performance of BIGSIR. This work not only provides a new dataset, but also highlights a new perspective for service recommendation, i.e. services as a bipartite network.