EPAN-SERec: Expertise preference-aware networks for software expert recommendations with knowledge graph

Abstract

The software knowledge community provides software developers with valuable knowledge of technologies, activities, tools and project management related to software development. However, a large number of unresolved questions and the lack of expert participation have become evident and critical challenges for the software knowledge community. To address the problems of label dependence, interactive data’s sparsity and unassociated knowledge in community-based software expert recommendation, we propose an Expertise Preference-Aware Network model for Software Expert Recommendation (EPAN-SERec) with knowledge graph. Firstly, the software knowledge graph is utilized as an auxiliary resource to provide domain knowledge representation. Secondly, we devise an expertise preference-learning framework by means of deep reinforcement learning that models the historical interactive information of experts and generate the expertise preference weight graph. To better learn expertise preference features, a graph convolutional network (GCN) model with integrated graph self-supervised learning is proposed to optimize the features representation. Finally, software knowledge entity embeddings with semantic information are obtained by exploiting the graph-embedding model, and the final features of question to be answered are obtained by fusing the expertise preference of experts. Extensive experiments on the dataset based on StackOverflow demonstrate that our approach achieves a better outcome than baseline models.