Plataforma de recomendación de contenidos para libros electrónicos inteligentes basadas en el comportamiento de los usuarios.

Jose Fernando López, E. R. Núnez Valdéz, J. M. Cueva, O. Sanjuán, B. C. Pelayo, C. Montenegro

Abstract


Un sistema de recomendación de contenidos basado en las relaciones colectivas de sus usuarios asociados en comunidades de lectores de una red social, permite construir un conocimiento colectivo que ayudan a recomendar de forma automática listas de contenidos a los usuarios de la plataforma social, de acuerdo a su comportamiento, preferencias y antecedentes de lectura. En este trabajo, proponemos un modelo para una plataforma de recomendación de contenidos basado en las acciones y comportamiento de los usuarios de libros electrónicos en una comunidad de lectores en la web que ayude a los usuarios a descubrir contenidos de su interés de forma automática y con un mínimo esfuerzo.


Full Text:

PDF (Español)

References


Adomavicius, G., R. Sankaranarayanan, et al. (2005). "Incorporating contextual information in recommender systems using a multidimensional approach.” ACM Trans. Inf. Syst. 23(1): 103-145.

G. and A. Tuzhilin (2005). "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions." IEEE Trans, on Knowl. and Data Eng. 17(6): 734-749.

Balabanovic, M. a. S., Yoav (1997). “Fab: content-based, collaborative recommendation.” Commun. ACM 40(3): 66-72.

Claypool, M., D. Brown, et al. (2001). “Inferring User Interest.” IEEE Internet Computing 5(6): 32-39.

González Crespo, R., O. Sanjuan Martínez, et al (2011).

“Recommendation System based on user interaction data applied to intelligent electronic books.” Comput. Hum. Behav. 27(4): 1445-1449.

Jawaheer, G., M. Szomszor, et al. (2010). Comparison of implicit

and explicitfeedback from an online music recommendation service. Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. Barcelona, Spain, ACM: 47-51.

Kautz, H., B. Sel man, et al. (1997). "Referral Web: combining social networks and collaborative filtering." Commun. ACM 40(3): 63-65.

Kelly, D. and J. Teevan (2003). "Implicit feedback for inferring user preference: a bibliography.” SIGIR Forum 37(2): 18-28.

Linden, G., B. Smith, et al. (2003). “Amazon.com recommendations: item-to-item collaborative filtering." Internet Computing, IEEE 7(1): 76-80.

Noor, S. and K. Martinez (2009). Using social data as context for making recommendations: an ontology based approach. Proceedings of the 1st Workshop on Context, Information and Ontologies.

Heraklion, Greece, ACM: 1-8.

Núñez Valdéz., E. R., O. Sanjuan Martínez, et al. (2010). First Steps towards Implicit Feedback for Recommender Systems in Electronic Books. Distributed Computing and Artificial Intelligence. A. de Leon F. de Carvalho, S. Rodríguez-González, J. De Paz Santana and J. Rodríguez, Springer Berlin / Heidelberg. 79: 61-64.

O’Donovan, J. and B. Smyth (2005). Trust in recommender systems. Proceedings of the 10th international conference on Intelligent user interfaces. San Diego, California, USA, ACM: 167-174.

Resnick, P., N. Iacovou, et al. (1994). GroupLens: an open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM conference on Computer supported cooperative work. Chapel Hill, North Carolina, United States, ACM: 175-186.

Resnick, P. and H. R. Varian (1997). “Recommender systems.” Commun. ACM 40(3): 56-58.

Sanjuan Martínez, O., Pelayo G-Bustelo,C, González Crespo, R., Torres Franco, E. (2009). “Using Recommendation System for E-learning Environments at degreelevel.” International Journal of Artificial Intelligence and Interactive Multimedia 1(2): 67-70.

Taghipour, N. and A. Kardan (2008). A hybrid web recommender system based on Q-learning. Proceedings of the 2008 ACM symposium on Applied computing. Fortaleza, Ceara, Brazil, ACM: 1164-1168.

Terveen, L, W. Hill, etal. (1997). "PHOAKS: a system for sharing recommendations.” Commun. ACM 40(3): 59-62.

Wang, P. (1998). "Why recommendation is special?" Workshop on Recommender Systems, part of the 15th National Conference on Artificial Intelligence: 111-113

Ziegler, C.-N., S. M. McNee, et al. (2005). Improving recommendation lists through topic diversification.

Proceedings of the 14th international conference on World Wide Web. Chiba, Japan, ACM: 22-32.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2014 TECCIENCIA