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Predicting Conversion Rate of APP Advertising with Machine Learning |
Zhao Yang(), Yuan Xini, Chen Yawen, Wu Liqiang |
School of Information Management, Wuhan University, Wuhan 430072, China |
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Abstract [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.
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Received: 26 July 2018
Published: 11 December 2018
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