<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martinez-Pabon, F.</style></author><author><style face="normal" font="default" size="100%">Ospina-Quintero, J. C.</style></author><author><style face="normal" font="default" size="100%">Ramirez-Gonzalez, G.</style></author><author><style face="normal" font="default" size="100%">Munoz-Organero, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recommending ads from trustworthy relationships in pervasive environments.</style></title><secondary-title><style face="normal" font="default" size="100%">Mobile Information Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.hindawi.com/journals/misy/2016/8593173/abs/</style></url></web-urls></urls><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;&lt;span style=&quot;font-family: &amp;quot;Minion W08 Regular_1167271&amp;quot;, Times; font-size: 17px; text-align: justify;&quot;&gt;The use of pervasive computing technologies for advertising purposes is an interesting emergent field for large, medium, and small companies. Although recommender systems have been a traditional solution to decrease users&amp;rsquo; cognitive effort to find good and personalized items, the classic collaborative filtering needs to include contextual information to be more effective. The inclusion of users&amp;rsquo; social context information in the recommendation algorithm, specifically trust in other users, may be a mechanism for obtaining ads&amp;rsquo; influence from other users in their closest social circle. However, there is no consensus about the variables to use during the trust inference process, and its integration into a classic collaborative filtering recommender system deserves a deeper research. On the other hand, the pervasive advertising domain demands a recommender system evaluation from a novelty/precision perspective. The improvement of the precision/novelty balance is not only a matter related to the recommendation algorithm itself but also a better recommendations&amp;rsquo; display strategy. In this paper, we propose a novel approach for a collaborative filtering recommender system based on trust, which was tested throughout a digital signage prototype using a multiscreen scheme for recommendations delivery to evaluate our proposal using a novelty/precision approach.&lt;/span&gt;&lt;/p&gt;
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