摘要
移动应用广告是互联网广告市场中一种主动的广告形式,它能够分析用户的兴趣爱好,并投其所好,精准投放广告,从而提高用户体验,为广告平台与广告主带来巨大的收益.因此,预测移动应用广告的转化率已成为一个非常重要的研究方向.本文以逻辑回归和两个梯度提升树模型为基础,使用堆叠和平均的集成思想,提出了两种集成模型--SXL和BLLX模型,解决了传统预测模型能力有限,无法精准预测转化率的问题.在腾讯2017社交广告比赛的数据集上的实验结果表明, SXL和BLLX两种模型能够有效地提高广告转化率的预测结果.
Mobile app advertising is a kind of initiative advertising in the Internet advertising market. It can analyze users’ interests and hobbies to target their interest advertisements, and then it improves their experience and brings huge benefits to advertising platforms and advertisers. Therefore, predicting the conversion rate of mobile app ads has become a significant research direction. Based on logistic regression and two gradient boosting tree models, this study proposes two integration models, SXL and BLLX, using the idea of stacking and averaging. It solves the problem that traditional prediction models have limited capabilities and cannot accurately predict the conversion rate. The experimental results on the dataset of Tencent 2017 social advertising competition show that the SXL and BLLX can effectively improve the prediction results of advertising conversion rate.
作者
彭赞
郑瑾
何鸿业
PENG Zan;ZHENG Jin;HE Hong-Ye(School of Information Science and Engineering,Central South University,Changsha 410083,China)
出处
《计算机系统应用》
2019年第11期245-252,共8页
Computer Systems & Applications
基金
国家自然科学基金(61379109)~~
关键词
广告转化率
集成模型
特征工程
梯度提升树
移动应用
adavertisement conversion aate
ensemble model
feature engineering
Gradient boosting tree
mobile App
