Web API Recommendation Based on Self-Attentional Neural Factorization Machines With Domain Interactions

Abstract

With the advances of service computing technology, there has been a significant focus on enhancing the relevance or accuracy of Web API recommendation for Mashup creation. In the multiple established works, factorization machines based models portray increased performance compared with traditional content or collaborative filtering techniques with regard to accuracy, which still has great room for improvement. Thus, this article takes a further step to propose a more expressive factorization machines model, i.e., Self-Attentional Neural Factorization Machines with Domain Interactions for Web API recommendation. Specifically, we introduce the concept of domain interaction to reduce the calculation complexity and capture the common characteristics of all features in one domain. Then, we leverage an implicit attention mechanism, that is, self-attention, to identify the importance of different feature interactions, instead of using explicit attention. Moreover, to promote expressive capability, deep neural networks are integrated into the transformed feature interactions following the self-attention propagation, capturing low and high order representations. In this approach, the importance of feature interactions can contribute to both low and high order relations. Multiple studies on a practical dataset indicates the inherent benefits of the method proposed in this study over the baselines for Web API recommendation.