Abstract
The sparsity issue in collaborative filtering (CF) systems which are essential for recommendation engines in online communities is tackled in a novel way in this work. The goal of this research is to increase the accuracy, recall, and F-measure of personalized suggestions in human resource management by using graph neural networks (GNNs) to find initial user clusters. The model shows how it may recommend relevant human resources based on project involvement using GitHub as a case study. The findings demonstrate that this approach not only successfully resolves the sparsity problem but also improves the precision of recommendations, offering substantial advantages to project managers involved in HR decision-making.
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