@book {Figueroa2019,
	title = {Recommender Systems based on Linked Data},
	year = {2019},
	pages = {1-183},
	publisher = {Editorial Universidad del Cauca},
	organization = {Editorial Universidad del Cauca},
	edition = {1},
	abstract = {<p>Linked Data principles have led to semantically interlink and connect different resources at data level regardless 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 \&ndash;LOD\&ndash; project1, more and more data have been published on the Web of Data, helping its growth and evolution. This book studied RS 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 algorithms 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.</p>
},
	keywords = {graph-based algorithms, linked data, Machine learning, Recommender Systems, Web of Data},
	isbn = {978-958-732-380-1},
	author = {Cristhian Figueroa and Juan Carlos Corrales and Maurizio Morisio and Christian Nicol{\'a}s Figueroa Mart{\'\i}nez},
	editor = {Mario Delgado-Noguera}
}
