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Название: Knowledge Recommendation Systems with Machine Intelligence Algorithms
Автор: Jarosław Protasiewicz
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 139
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB
Knowledge recommendation is an urgent and timely topic encountered in research and information services. There is a strongly compelling and urgent need: the modern economy badly requires highly skilled professionals, researchers, and innovators, which enables opportunities to gain competitive advantages and assist in managing financial resources and available goods, as well as carrying out fundamental and applied research more effectively. The design, development, and implementation of the two representative IT systems discussed in the book supplemented with content-based recommendation algorithms illustrate how the paradigm and theory of knowledge recommendation work in practice. This also includes a way of the development and practical application of selected heuristics and Machine Learning/machine intelligence algorithms that aim to create individuals’ expertise profiles and to deliver ways of evaluating enterprise innovation. The book contains an original material and is unique in many ways. The prudent and though-out selection and the exposure of the topics, depth of coverage of the subject matter, and original insights are the focal features of the book. New and promising directions and techniques of Machine Learning applied to knowledge recommendation are original.
Автор: Jarosław Protasiewicz
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 139
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB
Knowledge recommendation is an urgent and timely topic encountered in research and information services. There is a strongly compelling and urgent need: the modern economy badly requires highly skilled professionals, researchers, and innovators, which enables opportunities to gain competitive advantages and assist in managing financial resources and available goods, as well as carrying out fundamental and applied research more effectively. The design, development, and implementation of the two representative IT systems discussed in the book supplemented with content-based recommendation algorithms illustrate how the paradigm and theory of knowledge recommendation work in practice. This also includes a way of the development and practical application of selected heuristics and Machine Learning/machine intelligence algorithms that aim to create individuals’ expertise profiles and to deliver ways of evaluating enterprise innovation. The book contains an original material and is unique in many ways. The prudent and though-out selection and the exposure of the topics, depth of coverage of the subject matter, and original insights are the focal features of the book. New and promising directions and techniques of Machine Learning applied to knowledge recommendation are original.