The top search results matching a user query that are displayed on the first
page are critical to the effectiveness and perception of a search system. A
search ranking system typically orders the results by independent
query-document scores to produce a slate of search results. However, such
unilateral scoring methods may fail to capture inter-document dependencies that
users are sensitive to, thus producing a sub-optimal slate. Further, in
practice, many real-world applications such as e-commerce search require
enforcing certain distributional criteria at the slate-level, due to business
objectives or long term user retention goals. Unilateral scoring of results
does not explicitly support optimizing for such objectives with respect to a
slate. Hence, solutions to the slate optimization problem must consider the
optimal selection and order of the documents, along with adherence to
slate-level distributional criteria. To that end, we propose a hybrid framework
extended from traditional slate optimization to solve the conditional slate
optimization problem. We introduce conditional sequential slate optimization
(CSSO), which jointly learns to optimize for traditional ranking metrics as
well as prescribed distribution criteria of documents within the slate. The
proposed method can be applied to practical real world problems such as
enforcing diversity in e-commerce search results, mitigating bias in top
results and personalization of results. Experiments on public datasets and
real-world data from e-commerce datasets show that CSSO outperforms popular
comparable ranking methods in terms of adherence to distributional criteria
while producing comparable or better relevance metrics.

本文提出了一种名为条件顺序选取滑板优化（CSSO）的混合框架，以更好地优化搜索排名系统，并促进电子商务搜索结果多样性、减缓顶部结果偏见和结果个性化等方面的实际问题，且在满足分布准则的同时产生出色的相关性度量。