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Recommender System Based on Linked Data / Cristhian Figueroa, Juan Carlos Corrales, and Maurizio Morisio.
- Format:
- Book
- Author/Creator:
- Figueroa, Cristhian, author.
- Corrales, Juan Carlos, author.
- Morisio, Maurizio, author.
- Language:
- English
- Subjects (All):
- Application software--Development.
- Application software.
- Data structures (Computer science).
- Data transmission systems.
- Physical Description:
- 1 online resource (186 pages)
- Edition:
- First edition.
- Place of Publication:
- Popayán, Colombia : Universidad del Cauca, [2019]
- Summary:
- Linked Data principles have led to semantically interlink and connect different resourcesat data levelregardless the structure, authoring, location etc. Data available on the Web using Linked Data hasresulted in a global data space called the Web of Data. Moreover, thanks to the efforts of the scientificcommunity and the W3C Linked Open Data (LOD) project, more and more data have been published onthe Web of Data, helping its growth and evolution.This book studies Recommender Systems that use LInked Data as a source for generatingrecommendations exploiting the large amount of available resources and the relationships betweenthem. Firts, a comprehensive state of the art is preseted in order to indetify and study frameworks andalgorithms for RS that rely on Linked Data. Second a framework named AlLied taht makes availableimplementations of the most used algortihms for resource recommendation based on Linked Data isdescribed.This framework is inteded to use and test the recommendation algorithms in various domains andcontexts, and to analyze their behavior under different conditions. Accordingly the framework is suitableto compare the results of these algorithms both in performance and relevance, and to enable thedevelopment of innovative applications on top of it.
- Notes:
- Description based on publisher supplied metadata and other sources.
- Includes bibliographical references and index.
- Description based on print version record.
- ISBN:
- 9789587323818
- 9587323815
- OCLC:
- 1253290826
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