An empirical comparison of social, collaborative filtering, and hybrid recommenders
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Association for Computing Machinery, Inc.Fecha de edición
2013-01Cita
10.1145/2414425.2414439
ACM Transactions on Intelligent Systems and Technology 4.1 (2013): 14
ISSN
2157-6904 (print); 2157-6912 (online)DOI
10.1145/2414425.2414439Financiado por
This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), Universidad Autonoma de Madrid (CCG10-UAM/TIC-5877), and the Scientific Computing Institute at UAM.Versión del editor
http://dx.doi.org/10.1145/2414425.2414439Materias
Collaborative filtering; Graph theory; Hybrid recommenders; Random walk; Recommender Systems; Social networks; User coverage; InformáticaNota
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Intelligent Systems and Technology, http://dx.doi.org/10.1145/2414425.2414439Derechos
© 2013 ACMResumen
In the Social Web, a number of diverse recommendation approaches have been proposed to exploit the user generated contents available in the Web, such as rating, tagging, and social networking information. In general, these approaches naturally require the availability of a wide amount of these user preferences. This may represent an important limitation for real applications, and may be somewhat unnoticed in studies focusing on overall precision, in which a failure to produce recommendations gets blurred when averaging the obtained results or, even worse, is just not accounted for, as users with no recommendations are typically excluded from the performance calculations. In this article, we propose a coverage metric that uncovers and compensates for the incompleteness of performance evaluations based only on precision. We use this metric together with precision metrics in an empirical comparison of several social, collaborative filtering, and hybrid recommenders. The obtained results show that a better balance between precision and coverage can be achieved by combining social-based filtering (high accuracy, low coverage) and collaborative filtering (low accuracy, high coverage) recommendation techniques. We thus explore several hybrid recommendation approaches to balance this trade-off. In particular, we compare, on the one hand, techniques integrating collaborative and social information into a single model, and on the other, linear combinations of recommenders. For the last approach, we also propose a novel strategy to dynamically adjust the weight of each recommender on a user-basis, utilizing graph measures as indicators of the target user's connectedness and relevance in a social network.
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Google Scholar:Bellogin Kouki, Alejandro
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Cantador Gutiérrez, Iván
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Díez Rubio, Fernando
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Castells Azpilicueta, Pablo
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Chavarriaga Bautista, Jesús Enrique
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