Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a
Service" cloud products in recent years. However, one of the major challenges
for ACS users is to improve the relevance of the search results for their
specific usecases. In this paper, we propose a novel method to find the optimal
ACS configuration that maximizes search relevance for a specific usecase
(product search, document search...) The proposed solution improves key online
marketplace metrics such as click through rates (CTR) by formulating the search
relevance problem as hyperparameter tuning. We have observed significant
improvements in real-world search call to action (CTA) rate in multiple
marketplaces by introducing optimized weights generated from the proposed
approach.

提出一种新的方法来找到最佳的 Azure Cognitive Search 配置，从而提高特定用例的搜索相关性，并通过将搜索相关性问题制定为超参数调整来改善关键的在线市场指标。

通过贝叶斯优化改进 Azure 认知搜索的搜索相关性

Improving search relevance of Azure Cognitive Search by Bayesian  optimization

An expression is derived for the classical free energy difference between two
configurations of a system, in terms of an ensemble of finite-time measurements
of the work performed in parametrically switching from one configuration to the
other. Two well-known equilibrium identities emerge as limiting cases of this
result.

综合有限时间内由一个构型切换到另一个构型时完成的功的一系列测量，推导出两种系统构型间的古典自由能差异表达式，并得出两个著名的平衡恒等式为此方程的极限情况。