Collaborative APIs recommendation for Artificial Intelligence of Things with information fusion

Abstract With the rapid development of Artificial Intelligence of Things (AIoT), many applications are developed and deployed, especially mobile applications and edge applications. Many softwares are developed to support such diverse applications. To facilitate the development of softwares for AIoT, developers and programmers usually rely on the employment of the mature application programming interfaces (APIs). However, it is difficult for developers to select the most suitable APIs to finish the development, decreasing the development efficiency and delaying the development schedule. To solve these problems, in this paper, we propose a collaborative framework for APIs recommendation for AIoT, and also propose a joint matrix factorization technique for information fusion to fully use different types of information in AIoT. The built framework uses the invocation records among users and APIs during the development. Using collaborative technologies, we yield the similarity relationships among users and among APIs and build three novel APIs recommendation models. We collected a real-world dataset and performed sufficient experiments. The experimental results demonstrate that our models produce superior recommendation accuracy.