Group preference based API recommendation via heterogeneous information network

Abstract

Heterogeneous information networks (HINs) are logical networks which involve multiple types of objects and multiple types of links denoting different relations. Previous API recommendation studies mainly focus on homogeneous networks or few kinds of relations rather than exploiting the rich heterogeneous information. In this paper, we propose a mashup group preference based API recommendation method for mashup creation. Based on the historical invocation experience, different semantic meanings behind meta paths, hybrid similarity measurement and the rich interactions among mashups, we build the API recommendation model and employ the model to make personalized API recommendation for different mashup developers. Extensive experimental results validate the effectiveness of our proposed approach in terms of different kinds of evaluation metrics.