BiGCNG: Bi-Path Graph Convolutional Neural Network with Gate Fusion for Person–Job Fit

Abstract

Person–Job Fit (PJF) serves as a core task of intelligent recruitment recommendation. However, existing graph-based PJF models rely on a fixed, single-path aggregation scheme, thereby failing to simultaneously capture global interaction statistics and local competency-matching signals from candidate–job bipartite graphs. To address this limitation, this work proposes Bi-path Graph Convolutional Neural Network with Gate Fusion (BiGCNG), a dual-path graph convolutional network with global learnable gate fusion, composed of three coordinated modules. First, the Shared Text Embedding Pre-processing Module (STEPM) generates unified node embeddings by fusing structured attributes and BERT contextual text features. Second, the Bi-path Graph Convolution Module (BiGCM) extracts multi-granularity graph representations via separate sum and max aggregation paths. Third, the lightweight Gate Fusion Module (GFM) balances two feature streams via a learnable global scalar gate. The model is optimized with regularized Bayesian Personalized Ranking (BPR) loss on highly sparse recruitment data (99.97% sparsity). BiGCNG is evaluated on the Zhilian dataset, a real-world Chinese recruitment dataset, and outperforms five mainstream baselines notably, increasing MRR@5 by 7.67% and NDCG@5 by 5.48% on the Candidate subset, 2.30% and 0.61% on the Job subset against the best baseline, respectively. Several visualizations and hyperparameter analysis jointly validate the effectiveness and robustness of dual-path propagation and gate fusion. This work provides an effective multi-granularity graph learning paradigm for intelligent talent recruitment matching.