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 IJCNS  Vol.3 No.1 , January 2010
A User Selection Method in Advertising System
Shiyi XIONG, Zhiqing LIN, Bo XIAO
Abstract: It’s important for mobile operators to recommend new services. Traditional method is sending advertising messages to all mobile users. But most of users who are not interested in these services treat the messages as Spam. This paper presents a method to find potential customers who are likely to accept the services. This method searchs the maximum frequent itemsets which indicate potential customers’ features from a large data set of users’ information, then find potential customers from those maximum frequent itemsets by using a bayesian network classifier. Experimental results demonstrate this method can select users with higher accuracy.
Keywords: User Selection, Maximum Frequent Itemsets, Bayesian Network
Cite this paper: nullS. XIONG, Z. LIN and B. XIAO, "A User Selection Method in Advertising System," International Journal of Communications, Network and System Sciences, Vol. 3 No. 1, 2010, pp. 54-58. doi: 10.4236/ijcns.2010.31007.
References

[1]   E. Rich, “User modeling via stereotypes [D],” Cognitive Science, Vol. 3, No. 4, pp. 329–354, 1979.

[2]   U. M. Fayyad, G. Piatecsky-Shapiro, and P. Smyth, “Advances in knowledge discovery and data mining [M],” California, AAAI/MIT Press, 1996.

[3]   R. Agrawal and M. Srikant, “Fast algorithms for mining association rules in large databases [R],” IBM Almaden Research Center, Tech Rep: RJ9839, 1994.

[4]   J. W. Han, J. Pei, Y. W. Yin, et al., “Mining frequent patterns without candidate generation: A frequent-pattern tree approach [J],” Data Mining and Knowledge Discovery, Vol. 8, No. 1, pp. 53–87, 2004.

[5]   F. V. Jensen, “An introduction to Bayesian networks,” Springer, New York, 1996.

[6]   M. desJarins, “Representing and reasoning with probabilistic knowledge: A Bayesian approach,” Uncertainty in Artificial Intelligence, pp. 227–234.

[7]   N. Friedman, D. Geiger, and M. Goldszmidt, “Bayesian network classifiers,” Machine Learning, pp.131–163, 1997.

[8]   G. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data [D],” Machine Learning, Vol. 9, No. 4, pp. 309–347, 1992.

 
 
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