3 results
Search Results
2. Best Match: New relevance search for PubMed.
- Author
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Fiorini, Nicolas, Canese, Kathi, Starchenko, Grisha, Kireev, Evgeny, Kim, Won, Miller, Vadim, Osipov, Maxim, Kholodov, Michael, Ismagilov, Rafis, Mohan, Sunil, Ostell, James, and Lu, Zhiyong
- Subjects
SEARCH engines ,SEARCH algorithms ,INTERNET searching ,DATA mining ,MEDICAL literature - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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3. A personalized channel recommendation and scheduling system considering both section video clips and full video clips
- Author
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SeungGwan Lee and Daeho Lee
- Subjects
Computer science ,Section (typography) ,Video Recording ,Social Sciences ,lcsh:Medicine ,02 engineering and technology ,computer.software_genre ,Geographical locations ,Machine Learning ,Database and Informatics Methods ,Learning and Memory ,0202 electrical engineering, electronic engineering, information engineering ,Data Mining ,Psychology ,Computer Networks ,CLIPS ,lcsh:Science ,Statistical Data ,computer.programming_language ,Multidisciplinary ,Multimedia ,Applied Mathematics ,Simulation and Modeling ,IPTV ,Scheduling system ,Physical Sciences ,Information Retrieval ,020201 artificial intelligence & image processing ,Information Technology ,Algorithms ,Statistics (Mathematics) ,Research Article ,Communication channel ,Computer and Information Sciences ,Schedule ,Minnesota ,Broadcasting ,Research and Analysis Methods ,Computer Communication Networks ,Artificial Intelligence ,Learning ,Humans ,Internet ,business.industry ,Communications Media ,lcsh:R ,Cognitive Psychology ,Biology and Life Sciences ,020207 software engineering ,Models, Theoretical ,United States ,North America ,Cognitive Science ,lcsh:Q ,People and places ,business ,computer ,Mathematics ,Neuroscience - Abstract
With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper.
- Published
- 2018
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