合同展示广告中槽位属性的预测与应用

Hanmin Wang, Xinglu Liu, Wai Kin Victor Chan
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引用次数: 0

摘要

在本文中,我们提出了一种隐式包含多个特征来描述两个变量之间关系的方法,称为趋势预测。该方法考虑了槽页视图与多个特征之间的关系。采用树模型和神经网络等预测方法构建不同广告时段的数据,并采用自编码器模型进行分类特征密度化。为了减少预测曲线的平滑时间,我们采用对数函数作为先验对预测曲线进行回归。最后,与我们之前的预测模型相比,趋势预测模型有效地提高了无约束混合整数规划模型下的整体页面浏览量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and Application of Slot Attributes In Contract Display Advertisement
In this paper, we propose a method that implicitly contains multiple features to describe the relationship between two variables, which was referred to as trend prediction. This method takes into account the relationship between the slot page view and multiple features. The data of different advertising slots are constructed by predicting methods such as tree model and neural network with category feature densification by auto-encoder model. To reduce the smooth time in the prediction curve, we take the logarithm function as a priori to regress the predicted curve. Finally, compared with our previous prediction model, the trend prediction model effectively improves the overall page view under the unconstrained mixed-integer programming model.
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