Biased Random Walk based Web API Recommendation in Heterogeneous Network

Benefit from the remarkable development of cloud computing, massive Web APIs have been published and numerous innovative mashups have emerged on the Internet. The rapid increase of available Web APIs brings a significant challenge on how to discover suitable APIs for mashup creation. Various approaches have been proposed to recommend Web APIs, which mainly focus on employing collaborative filtering techniques such as matrix factorization and deep learning techniques such as graph neural network. However, existing studies suffer from a few limitations: collaborative filtering based methods are not able to capture the high-order connectivity information between APIs and mashups, and deep learning based methods are not expressive enough to capture the diversity of connectivity patterns due to the simple graph structure. To tackle the limitations, we proposes a biased random walk based Web API recommendation approach. We first construct a heterogeneous network to model different types of connectivity. And then we design a strategy in terms of neighbor nodes' types to guide the biased random walk and to capture diversity of high-order connectivity patterns. Finally we generate candidate Web APIs for mashups based on their embedding vectors. Experiments conducted on a real-world dataset show that our approach improves in precision by 13.89%, in recall by 17.16%, in F1 by 15.86%, and in NDCG by 2.85%.