  In real-world search, recommendation, and advertising systems, the
multi-stage ranking architecture is commonly adopted. Such architecture usually
consists of matching, pre-ranking, ranking, and re-ranking stages. In the
pre-ranking stage, vector-product based models with representation-focused
architecture are commonly adopted to account for system efficiency. However, it
brings a significant loss to the effectiveness of the system. In this paper, a
novel pre-ranking approach is proposed which supports complicated models with
interaction-focused architecture. It achieves a better tradeoff between
effectiveness and efficiency by utilizing the proposed learnable Feature
Selection method based on feature Complexity and variational Dropout (FSCD).
Evaluations in a real-world e-commerce sponsored search system for a search
engine demonstrate that utilizing the proposed pre-ranking, the effectiveness
of the system is significantly improved. Moreover, compared to the systems with
conventional pre-ranking models, an identical amount of computational resource
is consumed.
