<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Iacopo Vagliano</style></author><author><style face="normal" font="default" size="100%">Cristhian Figueroa</style></author><author><style face="normal" font="default" size="100%">Oscar Rodriguez</style></author><author><style face="normal" font="default" size="100%">M Torchiano</style></author><author><style face="normal" font="default" size="100%">C Faron-Zucker</style></author><author><style face="normal" font="default" size="100%">M Morisio</style></author><author><style face="normal" font="default" size="100%">Christian Nicolás Figueroa Martínez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data</style></title><secondary-title><style face="normal" font="default" size="100%">3rd Workshop on New Trends in Content-based Recommender Systems - CBRecSys 2016</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DBpedia</style></keyword><keyword><style  face="normal" font="default" size="100%">linked data</style></keyword><keyword><style  face="normal" font="default" size="100%">Recommender System</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1673/paper6.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR Workshop Proceedings</style></publisher><pages><style face="normal" font="default" size="100%">31-39</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Web of Data is an interconnected global dataspace in which discovering resources related to a given resource and recommend relevant ones is still an open research area. This work describes a new recommendation algorithm based on structured data published on the Web (Linked Data). The algorithm exploits existing relationships between resources by dynamically analyzing both the categories to which they belong to and their explicit references to other resources. A user study conducted to evaluate the algorithm showed that our algorithm provides more novel recommendations than other state-of-the-art algorithms and keeps a satisfy- ing prediction accuracy. The algorithm has been applied in a mobile application to recommend movies by relying on DBpedia (the Linked Data version of Wikipedia), although it could be applied to other datasets on the Web of Data.&lt;/p&gt;
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