@article{
 title = {Social factors in group recommender systems},
 type = {article},
 year = {2013},
 keywords = {Memory,Personality,Recommender systems,Social networks,Trust},
 volume = {4},
 id = {0a072838-966b-383a-9045-1ff56bf34c7a},
 created = {2016-05-12T09:12:14.000Z},
 file_attached = {false},
 profile_id = {9b41408a-6b73-3771-9777-d165eecfa568},
 last_modified = {2019-08-20T10:44:23.395Z},
 read = {false},
 starred = {true},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations. © 2013 ACM.},
 bibtype = {article},
 author = {Quijano-Sanchez, L. and Recio-Garcia, J.A. and Diaz-Agudo, B. and Jimenez-Diaz, Guillermo},
 doi = {10.1145/2414425.2414433},
 journal = {ACM Transactions on Intelligent Systems and Technology},
 number = {1}
}

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