Singular value decomposition‐based behavior‐aware cloud service application programming interfaces recommendation for large‐scale software cloud directory platforms

Abstract

With the development of Internet technology and the cloud service industry, an increasing number of application programming interfaces (APIs) hosted in the cloud has been made publicly available. To facilitate cloud service APIs vendors and buyers, some large-scale software cloud directory platforms have been established. Nevertheless, it is difficult for users to choose for renting from a massive number of cloud service APIs with similar functionalities in a software cloud directory platform.Recent efforts in building cloud service APIs recommender systems can help address this challenge. Relevant existing recommendation approaches are designed based on requirement election techniques to identify users' preferences to the quality of service (QoS) of the APIs. In particular, users' preferences are mainly obtained through their self-description, in which users sometimes cannot accurately and completely express their preferences. In this article, we propose SVD-APIR, a singular value decomposition (SVD)-based behavior-aware cloud service APIs recommendation approach for large-scale software cloud directory platforms. In SVD-APIR, users' historical behavior information is captured and APIs' association information is analyzed to identify the users' potential preferences to the APIs with specific QoS. A unified SVD model is utilized to prioritize the users preferred APIs. Experimental evaluation results conducted on WS-Dream dataset demonstrate the effectiveness and efficiency of the proposed approach.