基于图注意力网络的开源社区问题解决参与者推荐

Graph attention network based participant recommendation for issue resolution in open source community

在开源社区中,开发者提出的问题能否得到快速与高质量的答复和解决决定着社区的活跃程度。因此,为新提交的问题寻找和推荐合适的问题解决参与者有助于社区的发展。根据开发者之间的协作关系记录与开发者参与问题的记录构建了双层图注意力网络的问题解决参与者推荐模型(GAT-UCG)。首先获取问题参与者的信息和开发者的互动信息,分别构建开发者问题参与图和开发者协作关系图。通过注意力机制对于边重新分配权重,最后根据输出层得到的问题节点嵌入表示进行问题参与者的top-N推荐。选取了GitHub流行仓库中的7352个问题进行了实验,实验结果表明,所提GAT-UCG模型在推荐准确率、召回率、F-score三个指标上均优于基线方法。

In the open source community,it’s essential to find and recommend suitable participants for newly initiated issues in order to solved the issues and develop the community.This paper proposed to construct a two-layer graph attention network participant recommendation model(GAT-UCG)based on the cooperative relationship records and the historical participated issues records of the developers.The method used to construct the model is obtaining the information of the problem participants and the interaction information of the developers,and built the developer problem participation graph and the developer collaboration relationship graph respectively,then redistributed the weights to the edges through the attention mechanism.Finally,it figured the top-N recommendation of the problem participants according to the issue node embedding representation obtained by the output layer.There were 7352 issues from popular GitHub repositories for experiments.The results show that the GAT-UCG model outperforms the baseline method in three indicators:recommendation accuracy,recall,and F-score.