Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extraction, pose significant challenges for large-scale deployment. This paper investigates how different configurations of feature selection and document filtering in neural cascade ranking systems influence the trade-off between computational cost and ranking performance.