Interactive Web API Recommendation via Exploring Mashup-API Interactions and Functional Description

With the advance of service computing technology, the number of Web APIs has risen dramatically over the Internet. Users tend to use Web APIs to achieve their business needs. However, it is difficult for users to find and select the desirable ones due to the plethora of Web APIs. To address this problem, some collaborative filtering-based Web API recommendation methods have been proposed even though their performance is still far from satisfaction, since they only rely on Mashup-API interactions and feature interactions are not considered in the recommendation model. To further improve the recommendation performance, this paper proposes an interactive Web API recommendation method via exploring both Mashup-API interactions and functional description documents of Mashups and Web APIs. Specifically, LightGCN is employed to derive the node representations for the Mashup-API interaction graph, and BERT model is used for the text representations of functional description documents. Furthermore, the two presentations of both the Mashup and Web API are concatenated as the input of ANFM (Attentional Neural Factorization Machine) model, in which low and high-order feature interactions are fully modeled and the weights of feature interactions are trained by attention mechanism. Solid experiments are conducted over a real-world dataset and the experimental results indicate that the proposed method outperforms the baseline methods.