摘要
为有效提升搜索广告的点击率预测效果,提出一种基于特征降维和深度置信网络的模型(KTDDBN)。针对传统方法还停留在探索广告特征间的线性关系的局限性,提出使用深度置信网络寻找广告特征间更加复杂的深层关联;提取广告特征后,采用K-means聚类以及张量分解对高维特征进行降维,利用深度置信网络挖掘高阶的特征组合,提高预测模型的效果。实验结果表明,该模型在一定程度上提升了广告点击率的预测效果。
To improve the click-through rate prediction result in search advertising,a model based on feature dimension reduction and deep belief network(KTDDBN)was proposed.To solve the limitation that traditional methods still explore the linear relationship of the advertising features,deep belief network was proposed to search the complex deep associations of advertising features.After extracting advertisement features,K-means clustering and tensor decomposition were used to reduce the dimensio-nality of high-dimensional features and deep belief network was used to excavate high-level feature combinations,improving the effectiveness of the prediction model.Experimental results show that the proposed model can effectively improve the accuracy of CTR prediction to a certain degree.
作者
杨长春
梅佳俊
吴云
顾寰
YANG Chang-chun;MEI Jia-jun;WU Yun;GU Huan(School of Information Science and Engineering, Changzhou University, Changzhou 213164, China)
出处
《计算机工程与设计》
北大核心
2018年第12期3700-3704,共5页
Computer Engineering and Design
基金
赛尔网络下一代互联网技术创新基金项目(NGII20160703)
关键词
点击率预测
计算广告学
张量分解
特征降维
深度置信网络
CTR prediction
computational advertising
tensor decomposition
feature dimension reduction
deep belief network