Attentional Neural Factorization Machine for Web Services Classification via Exploring Content and Structural Semantics

Abstract

Due to the rapid development of Web 2.0, a lot of Web services emerge over the Internet. How to efficiently manage Web services through classification is very important for Web service discovery. Although there have been a lot of works on Web services classification, they still have drawbacks. On one side, the feature extraction from the service repository is insufficient, which will influence the classification accuracy no matter what classification model is used. On the other side, the extracted features are used with the same weights and they lack depth fusion when training the classification model. In real-world application scenarios, different feature interactions often have different predictive capabilities, and not all feature interactions contain useful or positive information for estimating the target. In addition, both low-and high-order feature interactions are usually underlain real-world data. To solve the problems above, this paper proposes a novel Web services classification approach via fully exploring and integrating the content and structural semantics of Web services. In the proposed approach, the content representation is explored with the BERT-based document embedding model, and the structural representation is explored with the Node2vec network embedding model. Finally, attentional neural factorization machine is used for both the deep fusion of features and Web services classification. A set of experiments are done on real-world datasets crawled from Programmable Web. And solid experimental results show that the proposed approach outperforms the state-of-the-art approach and the other baselines.