Learning to Ask Screening Questions for Job Postings
Table 7. Question ranking feature ablation study.
['Model', 'AUROC', 'Precision @1', 'Precision @3', 'Recall @1', 'Recall @3', 'NDCG @1', 'NDCG @3']
Ranking Feature Ablation Study. In this experiment, we study which feature group contributes the most to the question ranking model. In Tab. The results show that all feature groups positively contribute to the model and should be retained. Note that adding interaction features only improve the performance marginally. This is because tree-based model can capture some feature correlations without explicit signal.