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020 _a9783031326967
_9978-3-031-32696-7
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082 0 4 _a620.00285
_223
100 1 _aProtasiewicz, Jarosław.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aKnowledge Recommendation Systems with Machine Intelligence Algorithms
_h[electronic resource] :
_bPeople and Innovations /
_cby Jarosław Protasiewicz.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aXV, 128 p. 51 illus., 11 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1101
500 _aAcceso multiusuario
505 0 _a1.Introduction -- 2.Literature review -- 3.Recommending reviewers and experts -- 4.Supporting innovativeness and information sharing -- 5.Selected algorithmic developments -- 6.Knowledge recommendation in practice -- 7.Conclusions.
520 _aKnowledge recommendation is an timely subject that is encountered frequently in research and information services. A compelling and urgent need exists for such systems: the modern economy is in dire need of highly-skilled professionals, researchers, and innovators, who create opportunities to gain competitive advantage and assist in the management of financial resources and available goods, as well as conducting fundamental and applied research more effectively. This book takes readers on a journey into the world of knowledge recommendation, and of systems of knowledge recommendation that use machine intelligence algorithms. It illustrates knowledge recommendation using two examples. The first is the recommendation of reviewers and experts who can evaluate manuscripts of academic articles, or of research and development project proposals. The second is innovation support, which involves bringing science and business together by recommending information that pertains to innovations, projects, prospective partners, experts, and conferences meaningfully. The book also describes the selection of the algorithms that transform data into information and then into knowledge, which is then used in the information systems. More specifically, recommendation and information extraction algorithms are used to acquire data, classify publications, identify (disambiguate) their authors, extract keywords, evaluate whether enterprises are innovative, and recommend knowledge. This book comprises original work and is unique in many ways. The systems and algorithms it presents are informed by contemporary solutions described in the literature - including many compelling, novel, and original aspects. The new and promising directions the book presents, as well as the techniques of machine learning applied to knowledge recommendation, are all original.
541 _fUABC ;
_cPerpetuidad
650 0 _aEngineering
_xData processing.
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 1 4 _aData Engineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031326950
776 0 8 _iPrinted edition:
_z9783031326974
776 0 8 _iPrinted edition:
_z9783031326981
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1101
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-32696-7
912 _aZDB-2-INR
912 _aZDB-2-SXIT
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