基于随机森林的在线广告点击购买预测  

Online Advertising Click Purchase Prediction Based on Random Forest

作　　者：苗新 王倚天 刘爽 MIAO Xin;WANG Yitian;LIU Shuang(School of Information Engineering,Shenyang University of Chemical Technology,Shenyang Liaoning 110142,China;Laboratory of Industrial Control Network and System,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang Liaoning 110016,China)

机构地区：[1]沈阳化工大学信息工程学院,辽宁沈阳110142 [2]中国科学院沈阳自动化研究所工业控制网络与系统研究室,辽宁沈阳110016

出　　处：《信息与电脑》2022年第12期54-56,共3页Information & Computer

摘　　要：随着移动互联网的不断发展,传统的线下广告形式逐渐转变为在线广告形式,互联网在线广告产业已经成为当下的热门行业。对在线广告点击购买行为进行精准预测可以有效减少广告方的营销成本,使其利润最大化,同时也能够为消费者推送更多合适的广告,提高用户使用体验,因此具有重要的现实意义。基于此,笔者提出一种基于随机森林的在线广告点击购买预测模型,通过调整算法的相关参数,对模型进行优化。实验结果表明,该模型的综合性能更好,拥有广泛的应用前景。With the continuous development of mobile Internet, the traditional offline advertising form has gradually transformed into today’s online advertising form. The Internet online advertising industry has become a hot industry at present. Accurate prediction of online advertising buying behavior can effectively reduce the cost of advertising and marketing for advertisers, maximize their profits, and at the same time push more suitable advertisements for consumers and improve user experiences, so it has certain practical significance. Based on the above purpose, this paper proposes an online advertising click-to-buy prediction model based on random forest algorithm. The model is optimized by adjusting the relevant parameters of the algorithm. The simulation results show that the model enjoys superior performance and has broad application prospects.

关 键 词：随机森林 在线广告 机器学习 购买预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]