RGPRec: A RAG‐Enhanced GNN for Personalized Task Recommendations in Open‐Source Communities

ABSTRACT Context Open‐source communities have become a crucial driver of technological innovation and developer growth. While numerous deep learning‐based recommender systems exist, they often fail to provide accurate recommendations for identifying suitable developers who meet the task requirements of project development due to project popularity imbalance and developer ability bias. Recent advances in retrieval‐augmented generation (RAG) have shown promise in enhancing data augmentation and personalization for recommender systems in scenarios with data sparsity. Objective This study aims to develop RGPRec, a RAG‐enhanced graph neural network (GNN) for personalized, debiased task recommendations in open‐source communities. The model targets task recommendation quality, fairness, and coverage capabilities for long‐tail developers. Methods RGPRec leverages RAG‐enhanced large language models (LLMs) to retrieve project‐related documents and enriched developer attributes to improve node representations through ego graph structural learning. It also recognizes the importance of long‐tail developers—less involved in projects but still contributing significantly—often overlooked by traditional recommender systems. Moreover, RGPRec reasonably evaluates each developer's ability through a multilabel assessment based on an ego GNN to calculate a debiased rating, generating more personalized and debiased task recommendations. Results Evaluation using real‐world data from two famous open‐source communities of SourceForge and GitHub, indicates that RGPRec significantly outperforms state‐of‐the‐art (SOTA) approaches in rating and ranking performances. In addition, ablation studies demonstrate the necessity of each component in the RGPRec model. Conclusion RGPRec effectively enhances task recommendation quality in open‐source communities. By integrating RAG and LLMs with GNNs, RGPRec achieves superior data augmentation and personalization compared to traditional approaches. Therefore, RGPRec can be a valuable tool for promoting inclusiveness and efficiency in collaborations within open‐source communities.