Web service composition has become a prevalent software development method that enables developing powerful Mashups by effectively combining Web services with different functions. However, as the number of Web services increases, it becomes challenging for developers to select appropriate services to develop Web applications that satisfy functional requirements. In order to recommend Web services considering user's preferences, a composition pattern-aware Web service recommendation method called EWACP-DeepFM is proposed, which combines the composition patterns between Web services and Mashups and the co-occurrence and popularity of Web services. By constructing a multi-dimensional feature matrix, which is further trained by the depth factorisation machine (DeepFM) model to learn potential link relationships between Web services and Mashup applications, and recommend Top-N best services for the target Mashup application. Experiments performed using the real datasets from ProgrammableWeb show that the proposed method outperforms others with better recommendation effectiveness.

Web 服务组合已成为一种流行的软件开发方法，它可以通过将 Web 服务与不同功能的有效组合来开发强大的 Mashup。但是，随着Web 服务数量的增加，开发人员选择合适的服务来开发满足功能需求的Web 应用程序变得具有挑战性。为了推荐考虑用户偏好的Web服务，提出了一种组合模式感知的Web服务推荐方法，称为EWACP-DeepFM，它结合了Web服务和Mashup之间的组合模式以及Web服务的共现和流行性。通过构建多维特征矩阵，通过深度分解机（DeepFM）模型进一步训练，学习Web服务和Mashup应用程序之间潜在的链接关系，并为目标Mashup应用程序推荐Top-N最佳服务。使用来自 ProgrammableWeb 的真实数据集进行的实验表明，所提出的方法优于其他方法，具有更好的推荐效果。