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.

Popularity bias in mobile APP recommendations skews results by over-prioritizing trending APPs, obscuring niche yet relevant alternatives, and conflating user interest with social conformity. To address this problem, we propose TDIC (short for Time-aware Disentanglement of Interest and Conformity), a novel causal framework tailored for personalized, debiased mobile APP suggestions. TDIC employs causal graph analysis to isolate user interest from conformity within interaction patterns, incorporates item quality to refine popularity judgments, and integrates temporal awareness to track the fluid nature of trends. TDIC disentangles authentic user intents from dynamic social influences, fostering unbiased and precise recommendations. Evaluated on two real-world datasets, MobileRec and Myket, TDIC demonstrates superior performance over state-of-the-art baselines, achieving gains of up to 17.24% in Recall@20 and 7.14% in NDCG@20 on MobileRec, and 2.55% in NDCG@50 on Myket. These outcomes highlight the crucial role of temporal dynamics and quality adjustments in mitigating popularity bias for mobile APP recommendations. Our code is available at https://github.com/ssea-lab/TDIC.

Mobile edge computing (MEC) is an emerging computing paradigm receiving growing attention. MEC significantly reduces latency by processing user requests on edge servers rather than cloud centers, making it ideal for real-time applications. However, due to resource limitations and user mobility, microservice requests may fail, especially when users move at high speeds. This paper introduces a new tool, MobileEdgeSim, to simulate microservice-oriented MEC environments. MobileEdgeSim integrates mobility prediction and service composition to enhance the pre-deployment of microservices. To evaluate MobileEdgeSim, we conducted a series of experiments comparing it to several state-of-the-art baseline approaches. We also conducted a user study to evaluate the tool’s effectiveness in real-world scenarios. Our results indicate that MobileEdgeSim significantly improves the success rate of both user requests and responses while reducing resource costs. MobileEdgeSim is available at https://github.com/ssea-lab/MobileEdgesim.