Effective Online Evaluation for Web Search

Alexey Drutsa, Gleb Gusev, E. Kharitonov, Denis Kulemyakin, P. Serdyukov, I. Yashkov
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引用次数: 3

Abstract

We present you a program of a balanced mix between an overview of academic achievements in the field of online evaluation and a portion of unique industrial practical experience shared by both the leading researchers and engineers from global Internet companies. First, we give basic knowledge from mathematical statistics. This is followed by foundations of main evaluation methods such as A/B testing, interleaving, and observational studies. Then, we share rich industrial experiences on constructing of an experimentation pipeline and evaluation metrics (emphasizing best practices and common pitfalls). A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that allow to conduct online experimentation efficiently. We invite software engineers, designers, analysts, and managers of web services and software products, as well as beginners, advanced specialists, and researchers to learn how to make web service development effectively data-driven.
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我们为您提供一个平衡组合的课程,既有在线评估领域的学术成就概述,也有来自全球互联网公司的领先研究人员和工程师分享的部分独特的工业实践经验。首先,我们给出数理统计的基础知识。其次是主要评估方法的基础,如A/B测试、交错和观察性研究。然后,我们在构建实验管道和评估度量(强调最佳实践和常见缺陷)方面分享了丰富的工业经验。我们教程的很大一部分致力于现代和最先进的技术(包括基于机器学习的技术),这些技术允许有效地进行在线实验。我们邀请web服务和软件产品的软件工程师、设计师、分析师和管理人员,以及初学者、高级专家和研究人员来学习如何使web服务开发有效地由数据驱动。
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