%A Zhao Yang,Yuan Xini,Chen Yawen,Wu Liqiang %T Predicting Conversion Rate of APP Advertising with Machine Learning %0 Journal Article %D 2018 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.0834 %P 2-9 %V 2 %N 11 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4574.shtml} %8 2018-11-25 %X

[Objective] This paper tries to predict the conversion rate of APP advertisements with the help of machine learning algorithms, aiming to improve the effectiveness of advertising and marketing activities. [Methods] First, we examined the characteristics of APP advertisements. Then, we applied four machine learning algorithms to predict their conversion rate. The proposed RF+LXFV model was built with Random Forest, Gradient Boosting Decision Tree, Random Forest, LightGBM, XGBoost, Vowpal Wabbit and Field-aware Factorization Machine. Finally, we evaluated the validity and accuracy of the new model with Tencent APP advertising data. [Results] The prediction results of the proposed model achieved higher accuracy than those of the single algorithm. [Limitations] We did not examine the impacts of advertising transformation delay on prediction. [Conclusions] The proposed RF+LXFV model could predict the conversion rate of APP advertising effectively.