聊城大学学报（自然科学版）2025，Vol.38Issue(4)：485-496,12.DOI:10.19728/j.issn1672-6634.2024070012
基于解耦合图卷积的属性补全开发者推荐算法
Attribute completion developer recommendation algorithm based on decoupled graph convolution
摘要
Abstract
The crowdsourced software development model harnesses global developer resources to enable efficient software development,yet information overload on platforms poses challenges in recommending suitable developers.Existing recommendation algorithms rely on developer attributes and interaction be-haviors,but incomplete developer profiles in crowdsourced platforms hinder recommendation perform-ance.To address this,we propose an Attribute-Completion Developer Recommendation algorithm via De-coupled Graph Convolution(ADD).ADD comprises two modules:an attribute inference module and an attribute completion module.The attribute inference module leverages decoupled graph convolution to de-duce developer attributes across multiple latent spaces,while the attribute completion module fills missing attributes using inferred results.The completed attributes are iteratively fed back into the inference mod-ule,forming a cyclic optimization process until convergence.Upon convergence,the model achieves peak attribute inference capability,generating attributes that closely reflect real-world scenarios.Experiments on real-world datasets demonstrate that ADD significantly outperforms state-of-the-art models in MAE,RMSE,Precision,and Recall metrics,effectively resolving information incompleteness in crowdsourced developer recommendations.关键词
开发者推荐/解耦表示学习/属性补全/众包软件开发Key words
developer recommendations/disentangled representation learning/attribute completion/crowdsourced software development分类
信息技术与安全科学引用本文复制引用
鲁彦,凌松松,钟婧,于旭..基于解耦合图卷积的属性补全开发者推荐算法[J].聊城大学学报（自然科学版）,2025,38(4):485-496,12.基金项目
国家自然科学基金项目(62172249,62472441)资助 （62172249,62472441）
