東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Knowledge recommendation systems wit...

Protasiewicz, Jarosław.

FindBook

Google Book

Amazon

博客來

Knowledge recommendation systems with machine intelligence algorithms = people and innovations /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Knowledge recommendation systems with machine intelligence algorithms/ by Jarosław Protasiewicz.
其他題名:	people and innovations /
作者:	Protasiewicz, Jarosław.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xv, 128 p. :ill. (some col.), digital ;24 cm.
內容註:	1.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.
Contained By:	Springer Nature eBook
標題:	Recommender systems (Information filtering) -
電子資源:	https://doi.org/10.1007/978-3-031-32696-7
ISBN:	9783031326967

Knowledge recommendation systems with machine intelligence algorithms = people and innovations /
Protasiewicz, Jarosław.

Knowledge recommendation systems with machine intelligence algorithmspeople and innovations /[electronic resource] :by Jarosław Protasiewicz. - Cham :Springer Nature Switzerland :2023. - xv, 128 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 11011860-9503 ;. - Studies in computational intelligence ;v. 1101..

1.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.

Knowledge 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.

ISBN: 9783031326967

Standard No.: 10.1007/978-3-031-32696-7doiSubjects--Topical Terms:

1002434
Recommender systems (Information filtering)

LC Class. No.: ZA3084 / .P76 2023

Dewey Class. No.: 005.56

Knowledge recommendation systems with machine intelligence algorithms = people and innovations /
LDR:03217nmm a2200337 a 4500 001 2334754
003 DE-He213
005 20230930215742.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031326967 $q (electronic bk.)
020 $a 9783031326950 $q (paper)
024 7 $a 10.1007/978-3-031-32696-7 $2 doi
035 $a 978-3-031-32696-7
040 $a GP $c GP
041 0 $a eng
050 4 $a ZA3084 $b .P76 2023
072 7 $a UN $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.56 $2 23
090 $a ZA3084 $b .P967 2023
100 1 $a Protasiewicz, Jarosław. $3 3666632
245 1 0 $a Knowledge recommendation systems with machine intelligence algorithms $h [electronic resource] : $b people and innovations / $c by Jarosław Protasiewicz.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a xv, 128 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1101
505 0 $a 1.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 $a Knowledge 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.
650 0 $a Recommender systems (Information filtering) $3 1002434
650 0 $a Artificial intelligence. $3 516317
650 0 $a Computer algorithms. $3 523872
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1101. $3 3666633
856 4 0 $u https://doi.org/10.1007/978-3-031-32696-7
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)