通过贝叶斯优化提升Azure Cognitive Search的搜索相关性
Improving search relevance of Azure Cognitive Search by Bayesian optimization
AI总结：
针对Azure Cognitive Search用户特定用例下搜索相关性提升难的问题，提出将搜索相关性问题建模为超参数调优、用贝叶斯优化找最优配置的方法，在真实场景中显著提升了CTA率等指标。
摘要：
近年来，Azure Cognitive Search（ACS，Azure认知搜索）已成为“搜索即服务”云产品中的主流竞争者。然而，ACS用户面临的主要挑战之一是针对其特定用例提升搜索结果的相关性。本文提出了一种新颖方法，用于找到最优的ACS配置，最大化特定用例（商品搜索、文档搜索等）的搜索相关性。该方案将搜索相关性问题建模为超参数调优问题，提升了点击率（CTR）等关键线上市场指标。我们在多个市场的真实搜索场景中观察到，引入该方法生成的优化权重后，行动号召（CTA）率有显著提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
