Web API Recommendation for Mashup Development Using Matrix Factorization on Integrated Content and Network-Based Service Clustering

Abstract

Finding appropriate web APIs to develop mashup services is becoming difficult because of increasing number of web APIs offered from different sources. If we can recommend relevant web APIs for a mashup service based on its requirements, it will help software developers to find suitable APIs easily instead of searching from thousands of web APIs. Although there are many existing methods to recommend web APIs for mashup services, their recommendation accuracies and diversities are still not high. We will present a novel approach in this paper to produce better web API recommendation results in terms of accuracy and diversity. It is a matrix factorization based API recommendation method for Mashup services. It uses a two-level topic model for clustering Mashup services. We used a dataset from programmableWeb to perform experiments and compared results of our method with other existing methods. Its evaluation results show that our matrix factorization based recommendation archives better API recommendation accuracy and diversity for Mashup services.