A Comparison of Recommender Systems for Mashup Composition

Web mashups are a new generation of applications created by composing contents and functions available through Web services and APIs. A central activity in mashup development is the retrieval and selection of components to be included in the composition. The adoption of recommender systems can alleviate some of the difficulties arising in this activity. Based on the results of an empirical study, this paper tries to shed light on the application of recommender systems to the mashup composition domain, and discusses the performance of different recommendation systems when applied to a very large collection of mashups and mashup components.