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Mathematical Problems in Engineering
Volume 2014, Article ID 818203, 9 pages
Research Article

Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network

1Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, China
2Key Laboratory of Software Engineering of Yunnan Province, Kunming 650091, China
3Department of Software Engineering, School of Software, Yunnan University, Kunming 650091, China

Received 23 April 2014; Accepted 12 July 2014; Published 24 July 2014

Academic Editor: Guangming Xie

Copyright © 2014 Zhipeng Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Most classical search engines choose and rank advertisements (ads) based on their click-through rates (CTRs). To predict an ad’s CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN) as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method.