While in a classification or a regression setting a label or a value is
assigned to each individual document, in a ranking setting we determine the
relevance ordering of the entire input document list. This difference leads to
the notion of relative relevance between documents in ranking. The majority of
the existing learning-to-rank algorithms model such relativity at the loss
level using pairwise or listwise loss functions. However, they are restricted
to univariate scoring functions, i.e., the relevance score of a document is
computed based on the document itself, regardless of other documents in the
list. To overcome this limitation, we propose a new framework for multivariate
scoring functions, in which the relevance score of a document is determined
jointly by multiple documents in the list. We refer to this framework as GSFs
-- groupwise scoring functions. We learn GSFs with a deep neural network
architecture, and demonstrate that several representative learning-to-rank
algorithms can be modeled as special cases in our framework. We conduct
evaluation using click logs from one of the largest commercial email search
engines, as well as a public benchmark dataset. In both cases, GSFs lead to
significant performance improvements, especially in the presence of sparse
textual features.

本研究提出了一种基于 groupwise scoring functions (GSFs) 的多元评分函数框架以改进排序学习，通过深度神经网络学习并在大规模商业邮箱数据集和公开基准数据集中得到了显著的性能提升。