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In real-world search, recommendation, and advertising systems, themulti-stage ranking architecture is commonly adopted. Such architecture usuallyconsists of matching, pre-ranking, ranking, and re-ranking stages. In thepre-ranking stage, vector-product based models with representation-focusedarchitecture are commonly adopted to account for system efficiency. However, itbrings a significant loss to the effectiveness of the system. In this paper, anovel pre-ranking approach is proposed which supports complicated models withinteraction-focused architecture. It achieves a better tradeoff betweeneffectiveness and efficiency by utilizing the proposed learnable FeatureSelection method based on feature Complexity and variational Dropout (FSCD).Evaluations in a real-world e-commerce sponsored search system for a searchengine demonstrate that utilizing the proposed pre-ranking, the effectivenessof the system is significantly improved. Moreover, compared to the systems withconventional pre-ranking models, an identical amount of computational resourceis consumed.

Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach