Explainable service recommendation for interactive mashup development counteracting biases

Abstract

Web services have been prevalently applied in many software development scenarios such as the development of many applications in the cloud, mobile networks, and Web. But Web services usually suffer from the serious issue of single functionality; thus in recent years, compositions of Web services, i.e., mashups, have become a popular choice, and have brought significant convenience in providing more comprehensive functionalities. But the diversity and number of Web services are expanding dramatically, resulting in an intractable challenge: how to effectively recommend Web services for mashup development. Researchers have proposed several recommendation approaches, but existing solutions are primarily applicable in a one-shot paradigm, which may introduce biases and usually lack explainability. In real-world scenarios, developers usually need to incorporate new Web services to address emerging challenges, implying that the development paradigm could be interactive. Moreover, existing approaches are prone to produce mediocre accuracy. To solve these issues, in this paper, we develop an innovative Multimodal Features-based Unbiased (MMFU) service recommendation framework for interactive mashup development, which takes full advantage of the multimodal features involved in the development procedure. Our MMFU framework encompasses two separate models developed to learn deep features from both text and graph structural information, and contains a feature fusion mechanism. Extensive experiments were performed on two real-world datasets, and the results revealed that the MMFU framework outperforms the compared existing state-of-the-art approaches, and has high explainability and the ability to counteract biases.