Online ad effectiveness evaluation with a two-stage method using a Gaussian filter and decision tree approach

作者：

Highlights：

• This research aims to help advertisers to optimize online advertising strategies.

• Effective online advertising strategies save cost and increase profits of advertisers.

• An effective online ads effectiveness evaluation model is established.

• Decision tree with cost matrix is applied to construct the model.

• Gaussian filter is applied to adjust distribution of online ads business data.

• Numerical analysis is employed to prove the effectiveness of our model.

摘要

•This research aims to help advertisers to optimize online advertising strategies.•Effective online advertising strategies save cost and increase profits of advertisers.•An effective online ads effectiveness evaluation model is established.•Decision tree with cost matrix is applied to construct the model.•Gaussian filter is applied to adjust distribution of online ads business data.•Numerical analysis is employed to prove the effectiveness of our model.

论文关键词：Decision tree,Effectiveness evaluation,Gaussian filter,Online ads

论文评审过程：Received 13 November 2018, Revised 22 April 2019, Accepted 22 April 2019, Available online 23 April 2019, Version of Record 7 May 2019.

论文官网地址：https://doi.org/10.1016/j.elerap.2019.100852