AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations.