Best Match: New relevance search for PubMed

PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature-about two articles are added every minute on average-finding and retrieving the most relevant papers for a given query is increasingly...

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Vydané v:PLoS biology Ročník 16; číslo 8; s. e2005343
Hlavní autori: Fiorini, Nicolas, Canese, Kathi, Starchenko, Grisha, Kireev, Evgeny, Kim, Won, Miller, Vadim, Osipov, Maxim, Kholodov, Michael, Ismagilov, Rafis, Mohan, Sunil, Ostell, James, Lu, Zhiyong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 28.08.2018
Public Library of Science (PLoS)
Predmet:
Advertising
Algorithms
Analysis
Biology and Life Sciences
Biotechnology
Computer and Information Sciences
Data mining
Data Mining - methods
Deep learning
Humans
Information retrieval
Information Storage and Retrieval - methods
Intelligence
International conferences
Knowledge management
Learning algorithms
Machine Learning
Machine tools
Medical literature
Medicine
Medicine and Health Sciences
MEDLINE
National libraries
People and places
Physical Sciences
PubMed
Queries
R&D
Relevance
Research & development
Research and Analysis Methods
Search algorithms
Search Engine
Search engines
Searches
Semantics
State of the art
User behavior
World Wide Web
United States > US
Maryland
ISSN:1545-7885, 1544-9173, 1545-7885
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Popis
Shrnutí:PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature-about two articles are added every minute on average-finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively.
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The authors have declared that no competing interests exist.
ISSN:1545-7885
1544-9173
1545-7885
DOI:10.1371/journal.pbio.2005343