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Effective Online Evaluation for Web Searchтезисы доклада Тезисы
Информация о цитировании статьи получена из
Web of Science,
Scopus
Дата последнего поиска статьи во внешних источниках: 23 января 2020 г.
- Авторы: Drutsa Alexey, Gusev Gleb, Kharitonov Eugene, Kulemyakin Denis, Serdyukov Pavel, Igor Yashkov
- Сборник: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
- Тезисы
- Год издания: 2019
- Место издания: ACM
- Первая страница: 1399
- Последняя страница: 1400
- DOI: 10.1145/3331184.3331378
- Аннотация: 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.
- Добавил в систему: Друца Алексей Валерьевич