Best Match: New relevance search for PubMed

• Published in: PLoS biology Vol. 16; no. 8; p. e2005343
• Main Authors: 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
• Language: English
• Published: United States Public Library of Science 28.08.2018; Public Library of Science (PLoS)
• Subjects: 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
• DOI: 10.1371/journal.pbio.2005343

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.