Abstract
The customized marketing is an increasing area where users are progressively demanding and saturated of massive advertising, which has a really low success rate and even discourage the purchase. Furthermore, another important issue is the smash hit of mobile applications in the most known platforms (Android and iPhone), with millions of downloads worldwide. Instaprom is a platform that joins both concepts in a mobile application available for Android and iPhone; it retrieves interesting instant promotions being close to the user but without invading the user’s e-mail. Nowadays, the platform sends promotions based on the customized preferences by the user inside the application, although the intelligent system proposed in this paper will provide a new approach for creating intelligent recommendations using similar users promotions and the navigation in the application information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bouneffouf, D.: Mobile Recommender Systems Methods: An Overview. CoRR (2013)
Çağil, G., Erdem, M.B.: An intelligent simulation model of online consumer behavior. J. Intell. Manuf. 23(4), 1015–1022 (2012)
Davidsson, C.: Mobile Application Recommender System (2010)
Gavalas, D., et al.: Mobile recommender systems in tourism. Journal of Network and Computer Applications (2013)
Godoy, D., Schiaffino, S.N., Amandi, A.: Integrating user modeling approaches into a framework for recommender agents (2010)
KdNuggets: Data Mining Methodology (2007), http://www.kdnuggets.com/polls/2007/data_mining_methodology.htm
Lakiotaki, K., et al.: Multicriteria User Modeling in Recommender Systems. IEEE Intelligent Systems 26(2), 64–76 (2011)
Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Evolving classification of agents behaviors: a general approach. In: Evolving Systems. Springer (2010)
Liu, W., et al.: Ontology-Based User Modeling for E-Commerce System. In: Third International Conference on Pervasive Computing and Applications, ICPCA 2008, pp. 260–263 (2008)
Martínez-López, F.J., Casillas, J.: Marketing Intelligent Systems for consumer behaviour modelling by a descriptive induction approach based on Genetic Fuzzy Systems. Industrial Marketing Management (2009)
Martínez-López, F.J., Casillas, J.: Mining uncertain data with multiobjective genetic fuzzy systems to be applied in consumer behaviour modelling. Expert Syst. Appl. 36(2), 1645–1659 (2009)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference (1998)
Prinzie, A., Poel, D.: Modeling complex longitudinal consumer behavior with Dynamic Bayesian networks: an Acquisition Pattern Analysis application. J. Intell. Inf. Syst., 283–304 (2011)
Rodríguez Rodríguez, A., Iglesias García, N., Quinteiro-González, J.M.: Modelling the psychographic behaviour of users using ontologies in web marketing services. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2011, Part I. LNCS, vol. 6927, pp. 121–128. Springer, Heidelberg (2012)
Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems (2010)
Weka doc.: http://weka.sourceforge.net/doc.dev/weka/clusterers/EM.html
Zhang, L., Chen, S., Hu, Q.: Dynamic Shape Modeling of Consumers’ Daily Load Based on Data Mining. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 712–719. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pozo, M.M., Iglesias, J.A., Ledezma, A.I. (2014). Intelligent Promotions Recommendation System for Instaprom Platform. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-10840-7_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
eBook Packages: Computer ScienceComputer Science (R0)