Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network
Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network
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DOI：
10.5120/ijca2016908332
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Dawei Yin;Shike Mei;Bin Cao;Jian-Tao Sun;Deepayan Chakrabarti;Deepak Agarwal;Fang Wanga;Warawut Suphamitmongkola;Haibin Cheng
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文献类型：
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作者：
Dawei Yin;Shike Mei;Bin Cao;Jian-Tao Sun;Deepayan Chakrabarti;Deepak Agarwal;Fang Wanga;Warawut Suphamitmongkola;Haibin Cheng
计算广告学是数字广告领域新兴的多维统计建模分支学科。每个用户每天访问的网页都在大幅增加，导致大量的显示广告（广告）。广告被用户点击的比率被称为广告的点击率（CTR）。该度量便于测量广告的有效性。将广告放置在适当的位置会导致CTR值的上升，这会影响客户访问广告的增长，从而增加广告交易所、发布商和广告商的利润率。因此，为了制定有效的广告投放策略，必须预测CTR指标。本文提出了一种预测模型，该模型使用统计机器学习回归技术，如多元线性回归（LR），泊松回归（PR）和支持向量回归（SVR），基于显示广告的广告投放的不同维度生成点击率。实验结果表明，通过超参数优化，基于SVR的点击模型在预测点击率方面具有更好的性能。
Computational Advertising is the currently emerging multidimensional statistical modeling sub-discipline in digital advertising industry. Web pages visited per user every day is considerably increasing, resulting in an enormous access to display advertisements (ads). The rate at which the ad is clicked by users is termed as the Click Through Rate (CTR) of an advertisement. This metric facilitates the measurement of the effectiveness of an advertisement. The placement of ads in appropriate location leads to the rise in the CTR value that influences the growth of customer access to advertisement resulting in increased profit rate for the ad exchange, publishers and advertisers. Thus it is imperative to predict the CTR metric in order to formulate an efficient ad placement strategy. This paper proposes a predictive model that generates the click through rate based on different dimensions of ad placement for display advertisements using statistical machine learning regression techniques such as multivariate linear regression (LR), poisson regression (PR) and support vector regression(SVR). The experiment result reports that SVR based click model outperforms in predicting CTR through hyperparameter optimization.