ScholarWorks@성균관대학교

초록

Real-time recruitment platforms with bidirectional matching systems pose unique challenges in predicting candidate interview engagement, with only about 3% of candidates reaching the waiting room stage. Unlike traditional recruitment prediction focusing on static resume-job matching, this study analyzes behavioral dynamics in live interview participation using 73,000 real-world candidate-job matching instances from a North American recruitment platform. We systematically evaluated 120 model configurations that combine five machine learning algorithms with multiple imbalance handling strategies. Our results demonstrate that LightGBM with a 1: 3 class weighting scheme and Tomek Links undersampling achieves the most stable performance across validation experiments (F1=0.597 ± 0.004, 95% CI: 0.585-0.609) with high test performance (Macro F1 =0.7971). Compared to the majority-class baseline classifier (Macro F1 = 0.4926), this corresponds to an absolute improvement of 0.3045. In addition, feature analysis highlights that user engagement metrics and temporal factors are as predictive as algorithmic match scores, with class weighting consistently outperforming aggressive resampling strategies. The study uncovers counterintuitive behavioral patterns, including a negative correlation between resume-job match scores and interview participation, suggesting algorithmic refinement opportunities. These findings provide actionable insights for the optimization of the recruitment platform and establish a methodological framework applicable to other real-time matching systems with extreme class imbalance.

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