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

Zhipeng Fang | Zendy; Kun Yue | Zendy; Jixian Zhang | Zendy; Dehai Zhang | Zendy; Weiyi Liu | Zendy

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

Author(s) -

Zhipeng Fang,

Kun Yue,

Jixian Zhang,

Dehai Zhang,

Weiyi Liu

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/818203

Subject(s) - computer science , rank (graph theory) , bayesian probability , click through rate , bayesian network , domain (mathematical analysis) , artificial intelligence , data mining , machine learning , information retrieval , mathematics , combinatorics , mathematical analysis

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

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