Abstract
Digital signage is a form of electronic display that presents information, advertising and other messages. With the merge of mobile technology and exponential growth of broadcasting network, an overwhelmingly amount of digital signage has been made available to dissimilar consumers. This paper presents an algorithm based on Naïve-Bayes technique to build a user modelling to be used for recommending a suitable signage to customers. Our goal is to personalize signage by choosing the proper products to the proper customers. This way is promising to present an automated algorithm to create an adaptable content which can be exchanged more easily and the signs can adapt to the context and audience.
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
Adler, M., Bradshaw, J.M., Mahan, M., Suri, N.: Applying Mobile Agents to Enable Dynamic, Context-Aware Interactions for Mobile Phone Users. In: Pierre, S., Glitho, R.H. (eds.) MATA 2001. LNCS, vol. 2164, p. 184. Springer, Heidelberg (2001)
Aggarwal, C.C., Wolf, J.L.: Horting Hatches an Egg: A New Graph-theoretic Approach to Collaborative Filtering. In: Proc. of the ACM KDD 1999 Conference, San Diego, CA, pp. 201–212 (1999)
Balasubramanian, S., Peterson, R.A., Jarvenpaa, S.L.: Exploring the Implications of M-Commerce for Markets and Marketing. Journal of the Academy of Marketing Science 30(4), 348–361 (2002)
Cotter, P., Smyth, B.: PTV: Intelligent personalized TV guides. In: Twelfth Conference on Innovative applications of Artificial Intelligence, pp. 957–964 (2000)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Bardesi, H.J., Razek, M.A.: Towards Smart Algorithm to Build Mobile Advertising. In: CSREA EEE, pp. 310–314 (2010)
IMAP Global System Framework – Business Model, Research Report (2003), http://www.imapproject.org/imapproject/downloadroot/ublic1/D2-2003.pdf (last accessed at December 20, 2003)
Kalt, T,. Croft, W. B.: A new probabilistic model of text classification and retrieval. Technical Report IR-78, University of Massachusetts Center for Intelligent Information Retrieval (1996), http://ciir.cs.umass.edu/publications/index.shtml
Koller, D., Sahami, M.: Hierarchically classifying products using very few products. In: Proc. of the 14th International Conference on Machine Learning (ICML 1997), pp. 170–178 (1997)
Leah, S., Bruce Croft, W.: Combining classifiers in text categorization. In: ACM SIGIR 1996 (1996)
Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison Wesley, Reading (2002)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3), 103–134 (2000)
Paolucci, M., Niu, Z, K. S. K.: Matchmaking to Support Intelligent Agents for Portfolio Management. In: Proc. of the AAAI (2000)
Pavlou, P.A., Stewart, D.W.: Measuring the Effects and Effectiveness of Interactive Advertising: A Research Agenda. Journal of Interactive Advertising 1(1) (2000)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)
Razek, M.A., Frasson, C., Kaltenbach, M.: Pyramid collaborative filtering technique for an intelligent autonomous guide agent. International Journal of Intelligent Systems 22(10), 1065–1154 (2007)
Razek, M., Frasson, C., Kaltenbach, M.: A Confident Agent: Toward More Effective Intelligent Distance Learning Environments. Accepted in ICMLA 2002, Las Vegas, USA, June 24-27 (2002)
Russel, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)
Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 42–49 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Razek, M.A., Frasson, C. (2011). User Modeling to Build Mobile Advertising Algorithm. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22410-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-22410-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22409-6
Online ISBN: 978-3-642-22410-2
eBook Packages: Computer ScienceComputer Science (R0)