A Developer Recommendation Method Based on Disentangled Graph Convolutional Network

Crowdsourcing Software Development (CSD) solves software development tasks by integrating resources from global developers. With more and more companies and developers moving onto CSD platforms, the information overload problem of the platform makes it difficult to recommend suitable developers for the software development task. The interaction behavior between developers and tasks is often the result of complex latent factors. Existing developer recommendation methods are mostly based on deep learning, where the feature representations ignores the influence of latent factors on interactive behavior, leading to learned feature representations that lack robustness and interpretability. To solve the above problems, we present a Developer Recommendation Method Based on Disentangled Graph Convolutional (DRDGC). Specifically, we use a disentangled graph convolutional network to separate the latent factors within the original features. Each latent factor contains specific information and is independent from each other, which makes the features constructed by the latent factors exhibit stronger robustness and interpretability. Extensive experiments results show that DRDGC can effectively recommend the right developer for the task and outperforms the baseline methods.