VLDB 2026 Research / reviewers in the wild / expert
Igor Yashkov
dblp:227/3364
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
evaluation |
0.4 | 1 | 2019 | Effective Online Evaluation for Web Search · SIGIR 2019 |
Information retrieval › evaluation
online evaluation |
0.4 | 1 | 2019 | Effective Online Evaluation for Web Search · SIGIR 2019 |
Information retrieval › evaluation › online evaluation
a/b testing |
0.1 | 1 | 2019 | Effective Online Evaluation for Web Search · SIGIR 2019 |
Information retrieval › retrieval evaluation
interleaving |
0.1 | 1 | 2019 | Effective Online Evaluation for Web Search · SIGIR 2019 |
Methods — techniques the papers use, named apart from their topics
mathematical statistics · 0.4machine learning · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Effective Online Evaluation for Web SearchabstractWe 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. Alexey Drutsa, Gleb Gusev, Eugene Kharitonov, Denis Kulemyakin, Pavel Serdyukov, Igor Yashkov |
SIGIR | 6 |