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Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network
Joint Authors
Zhang, Dehai
Liu, Weiyi
Yue, Kun
Fang, Zhipeng
Zhang, Jixian
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Fang, Zhipeng& Yue, Kun& Zhang, Jixian& Zhang, Dehai& Liu, Weiyi. 2014. Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-500554
Modern Language Association (MLA)
Fang, Zhipeng…[et al.]. Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network. Mathematical Problems in Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-500554
American Medical Association (AMA)
Fang, Zhipeng& Yue, Kun& Zhang, Jixian& Zhang, Dehai& Liu, Weiyi. Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-500554
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-500554