000088182 001__ 88182
000088182 005__ 20210520140803.0
000088182 037__ $$aTESIS-2020-042
000088182 041__ $$aeng
000088182 080__ $$a004; 004.6
000088182 1001_ $$aRodríguez Hernández, Mª del Carmen
000088182 24500 $$aContext-Aware Recommendation Systems in Mobile  Environments
000088182 260__ $$aZaragoza$$bUniversidad de Zaragoza, Prensas de la Universidad$$c2017
000088182 300__ $$a321
000088182 4900_ $$aTesis de la Universidad de Zaragoza$$v2020-42$$x2254-7606
000088182 500__ $$aPresentado:  20 12 2017
000088182 502__ $$aTesis-Univ. Zaragoza, Informática e Ingeniería de Sistemas, 2017$$bZaragoza, Universidad de Zaragoza$$c2017
000088182 506__ $$aall-rights-reserved
000088182 520__ $$aNowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.<br />The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br />
000088182 520__ $$a<br />
000088182 521__ $$97100$$aPrograma de Doctorado en Ingeniería de Sistemas e Informática
000088182 6531_ $$abases de datos
000088182 6531_ $$adiseño y componentes de sistemas de informacion
000088182 700__ $$aIlarri Artigas, Sergio$$edir.
000088182 7102_ $$aUniversidad de Zaragoza$$bInformática e Ingeniería de Sistemas
000088182 830__ $$9512
000088182 8560_ $$ftdr@unizar.es
000088182 8564_ $$s25345548$$uhttps://zaguan.unizar.es/record/88182/files/TESIS-2020-042.pdf$$zTexto completo (eng)
000088182 909CO $$ooai:zaguan.unizar.es:88182$$pdriver
000088182 909co $$ptesis
000088182 9102_ $$a$$bInformática e Ingeniería de Sistemas
000088182 980__ $$aTESIS