  Learning-to-rank (LTR) is a set of supervised machine learning algorithms
that aim at generating optimal ranking order over a list of items. A lot of
ranking models have been studied during the past decades. And most of them
treat each query document pair independently during training and inference.
Recently, there are a few methods have been proposed which focused on mining
information across ranking candidates list for further improvements, such as
learning multivariant scoring function or learning contextual embedding.
However, these methods usually greatly increase computational cost during
online inference, especially when with large candidates size in real-world web
search systems. What's more, there are few studies that focus on novel design
of model structure for leveraging information across ranking candidates. In
this work, we propose an effective and efficient method named as SERank which
is a Sequencewise Ranking model by using Squeeze-and-Excitation network to take
advantage of cross-document information. Moreover, we examine our proposed
methods on several public benchmark datasets, as well as click logs collected
from a commercial Question Answering search engine, Zhihu. In addition, we also
conduct online A/B testing at Zhihu search engine to further verify the
proposed approach. Results on both offline datasets and online A/B testing
demonstrate that our method contributes to a significant improvement.
