@article {533,
	title = {Recommending ads from trustworthy relationships in pervasive environments.},
	journal = {Mobile Information Systems},
	year = {2016},
	abstract = {<p class="rtejustify"><span style="font-family: \&quot;Minion W08 Regular_1167271\&quot;, Times; font-size: 17px; text-align: justify;">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\&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\&rsquo; social context information in the recommendation algorithm, specifically trust in other users, may be a mechanism for obtaining ads\&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\&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.</span></p>
},
	issn = {1574-017X},
	url = {https://www.hindawi.com/journals/misy/2016/8593173/abs/},
	author = {Martinez-Pabon, F. and Ospina-Quintero, J. C. and Ramirez-Gonzalez, G. and Munoz-Organero, M}
}
