Research on Effect Evaluation of Online Advertisement Based on Resampling Method

Abstract

With the rapid development of the Internet, the online advertising market has become larger and larger. Online advertisers often execute their advertising strategies based on the effect of online advertisements, so it is necessary to evaluate the advertising effect because it determines whether advertisers can display effective advertisements continually and remove ineffective advertisements timely. In practical scenarios, the quantity of ineffective online advertisements is always larger than that of effective online advertisements. The imbalanced distribution of them will bring serious bias to the evaluation models. We propose an improved undersampling method based on clustering (termed UBOC) to overcome the data imbalance. It can balance the advertising data into a more suitable data distribution. In addition, we adopt a new evaluation index for the effect evaluation of online advertisements based on C5.0 decision tree. Experimental results indicate the excellent performance of UBOC and the practical application of evaluation index for online advertisements. They can provide an effective evaluation of online advertisements and achieve the early removal of ineffective advertisements for advertisers, which will greatly increase the revenue brought by advertisements.