2 options
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 resources at data level regardless of the structure, authoring, location etc. Data available on the Web using Linked Data has resulted in a global data space called the Web of Data. Moreover, thanks to the efforts of the scientific community and the W3C Linked Open Data (LOD) project, more and more data have been published on the Web of Data, helping its growth and evolution. This book studies Recommender Systems that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships between them. First, a comprehensive state of the art is presented in order to identify and study frameworks and algorithms for RS that rely on Linked Data. Second a framework named Allied that makes available implementations of the most used algortihms for resource recommendation based on Linked Data is described. This framework is intended to use and test the recommendation algorithms in various domains and contexts, and to analyze their behavior under different conditions. Accordingly the framework is suitable to compare the results of these algorithms both in performance and relevance, and to enable the development of innovative applications on top of it.
- Notes:
- Includes bibliographical references and index.
- Description based on publisher supplied metadata and other sources.
- Description based on print version record.
- ISBN:
- 9789587323818
- 9587323815
- OCLC:
- 1253290826
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.