将自研搜索引擎迁移到OpenSearch，保持性能并提升用户参与度。
Migrating a Job Search Relevance Function
- 用众包标注查询-职位对，构建高质量评估数据集。
- 通过贝叶斯优化调优新检索算法，匹配原系统逻辑并新增功能。
- 线上测试显示性能持平，部分互动指标提升，收入影响极小。
本文描述了将自研C++搜索引擎迁移至OpenSearch的过程，旨在在最小化业务指标影响的前提下保持并提升搜索性能。为支持迁移，我们冻结了职位语料库，并在低库存区域执行查询，捕获高、低质量搜索结果的代表性混合样本。这些查询-职位对由众包标注员根据定制评分标准进行标注，以反映相关性和用户满意度。利用贝叶斯优化，在OpenSearch上微调新的检索算法，复现原引擎的关键逻辑，并在必要处引入新功能。通过大量线上测试，证明新系统表现与原系统相当，特定互动指标有所提升，对收入影响可忽略。
原文摘要 · Abstract (English)
In this paper, we describe the migration of a homebrewed C++ search engine to OpenSearch, aimed at preserving and improving search performance with minimal impact on business metrics. To facilitate the migration, we froze our job corpus and executed queries in low inventory locations to capture a representative mixture of high- and low-quality search results. These query-job pairs were labeled by crowd-sourced annotators using a custom rubric designed to reflect relevance and user satisfaction. Leveraging Bayesian optimization, we fine-tuned a new retrieval algorithm on OpenSearch, replicating key components of the original engine's logic while introducing new functionality where necessary. Through extensive online testing, we demonstrated that the new system performed on par with the original, showing improvements in specific engagement metrics, with negligible effects on revenue.
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