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.

翻译：在现实世界的搜索、建议和广告系统中,通常采用多阶段排名结构,这种结构通常包括匹配、排位前、排位和重新排位等阶段。在排位前阶段,通常采用基于病媒产品的模式,并采用注重代表性的结构,以顾及系统效率。然而,这给系统的效率带来重大损失。在本文中,提出了一种新的排位前办法,支持以互动为重点的结构的复杂模式。通过利用基于特征复杂性和变异性辍学(FSCD)的拟议可学习特征选择方法,在效果和效率之间实现更好的平衡。在现实世界电子商务赞助的搜索引擎搜索系统中进行的评估表明,利用拟议的排位前,系统的有效性得到显著提高。此外,与传统的排位前模式相比,使用相同的计算资源数量。