<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cristhian Figueroa</style></author><author><style face="normal" font="default" size="100%">Iacopo Vagliano</style></author><author><style face="normal" font="default" size="100%">Oscar Rodr&amp;iacute;guez Rocha</style></author><author><style face="normal" font="default" size="100%">Marco Torchiano</style></author><author><style face="normal" font="default" size="100%">Catherine Faron Zucker</style></author><author><style face="normal" font="default" size="100%">Juan Carlos Corrales</style></author><author><style face="normal" font="default" size="100%">Maurizio Morisio</style></author><author><style face="normal" font="default" size="100%">Christian Nicolás Figueroa Martínez</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Miltiadis D. Lytras</style></author><author><style face="normal" font="default" size="100%">Naif Aljohani</style></author><author><style face="normal" font="default" size="100%">Ernesto Damiani</style></author><author><style face="normal" font="default" size="100%">Kwok Tai Chui</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web Science and Real-World Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7186-5 https://www.igi-global.com/gateway/chapter/full-text-html/215060 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7186-5.ch002</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">IGI Global</style></edition><publisher><style face="normal" font="default" size="100%">IGI Global</style></publisher><pages><style face="normal" font="default" size="100%">18-47</style></pages><isbn><style face="normal" font="default" size="100%">9781522571865</style></isbn><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Data published on the Web following the Linked Data principles has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use Linked Data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for Linked Data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset.&lt;/p&gt;
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