Abstract: Computational advertising is a kind of advertising mechanism which has the capability to find the most suitable ads for given users and web content, so as to advertises them accurately. Therefore, estimating click-through rate (CTR) precisely makes significant difference in the efficiency of advertising on the Internet. Ad click-through rate prediction is to estimate CTR with click log, which is influenced by the nature features of ad, the position, the page information, user properties, the reputation of advertisers and such other factors. This paper is aimed to illustrate useful CTR prediction models, including CTR models for ads of abundant history data, CTR models for rare ads or new ads and some optimization models. Finally, the implementation methods with real data set were demonstrated as examples.

Key words: computational advertisement, click-through rate, logistic regression, Bayes method