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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Veröffentlicht in:	PLoS biology Jg. 16; H. 8; S. e2005343
Hauptverfasser:	Fiorini, Nicolas, Canese, Kathi, Starchenko, Grisha, Kireev, Evgeny, Kim, Won, Miller, Vadim, Osipov, Maxim, Kholodov, Michael, Ismagilov, Rafis, Mohan, Sunil, Ostell, James, Lu, Zhiyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 28.08.2018 Public Library of Science (PLoS)
Schlagworte:	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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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Kim, Won Starchenko, Grisha Kholodov, Michael Osipov, Maxim Miller, Vadim Ostell, James Lu, Zhiyong Kireev, Evgeny Mohan, Sunil Fiorini, Nicolas Canese, Kathi Ismagilov, Rafis
AuthorAffiliation	National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
AuthorAffiliation_xml	– name: National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
Author_xml	– sequence: 1 givenname: Nicolas orcidid: 0000-0002-9260-1326 surname: Fiorini fullname: Fiorini, Nicolas – sequence: 2 givenname: Kathi surname: Canese fullname: Canese, Kathi – sequence: 3 givenname: Grisha surname: Starchenko fullname: Starchenko, Grisha – sequence: 4 givenname: Evgeny surname: Kireev fullname: Kireev, Evgeny – sequence: 5 givenname: Won surname: Kim fullname: Kim, Won – sequence: 6 givenname: Vadim surname: Miller fullname: Miller, Vadim – sequence: 7 givenname: Maxim surname: Osipov fullname: Osipov, Maxim – sequence: 8 givenname: Michael surname: Kholodov fullname: Kholodov, Michael – sequence: 9 givenname: Rafis surname: Ismagilov fullname: Ismagilov, Rafis – sequence: 10 givenname: Sunil surname: Mohan fullname: Mohan, Sunil – sequence: 11 givenname: James surname: Ostell fullname: Ostell, James – sequence: 12 givenname: Zhiyong surname: Lu fullname: Lu, Zhiyong
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/30153250$$D View this record in MEDLINE/PubMed
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Title	Best Match: New relevance search for PubMed
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