[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.
(IResearch. The Scale of Online Advertising is 290.27 Billion Yuan, Occupy a Layer Proportion than Searchads [OL]. [2018-01-22].
[2]
张爽. 移动互联网时代的APP广告浅析[J]. 理财:经纶, 2017(10): 57-60.
[2]
(Zhang Shuang.Analysis of APP Advertising in the Age of Mobile Internet[J]. Finacial Management, 2017(10): 57-60.)
[3]
Cheng H, Cantú-Paz E.Personalized Click Prediction in Sponsored Search[C]// Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 2010: 351-360.
[4]
Tagami Y, Ono S, Yamamoto K, et al. CTR Prediction for Contextual Advertising: Learning-to-Rank Approach[C]// Proceedings of the 7th International Workshop on Data Mining for Online Advertising. ACM, 2013: Ariticle No.4.
(Li Chunhong, Wu Ying, Qin Chaoyong.Research on Search Engine Advertisement Click Rate Prediction Model Based on LASSO Variable Selection Method[J]. Journal of Applied Statistics and Management, 2016, 35(5): 803-809.)
doi: 10.13860/j.cnki.sltj.20160922-022
(Zhang Zhiqiang, Zhou Yong, Xie Xiaoqin, et al.Research on Advertising Click-Through Rate Estimation Based on Feature Learning[J]. Chinese Journal of Computers, 2016, 39(4): 780-794.)
doi: 10.11897/SP.J.1016.2016.00780
[7]
Chapelle O, Manavoglu E, Rosales R.Simple and Scalable Response Prediction for Display Advertising[J]. ACM Transactions on Intelligent Systems & Technology, 2014, 5(4): 1-34.
doi: 10.1145/2532128
[8]
Ahmed A, Low Y, Aly M, et al.Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting[C]// Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011: 114-122.
[9]
Richardson M, Dominowska E, Ragno R.Predicting Clicks: Estimating the Click-through Rate for New Ads[C]// Proceedings of the 16th International Conference on World Wide Web. ACM, 2007: 521-530.
[10]
单丽莉. 基于张量分解的实时竞价广告响应预测方法[D]. 哈尔滨: 哈尔滨工业大学, 2016.
[10]
(Shan Lili.Response Prediction on Real Time Bidding via Tensor Factorization[D]. Harbin: Harbin Institute of Technology. 2016.)
[11]
Juan Y, Zhuang Y, Chin W S, et al.Field-aware Factorization Machines for CTR Prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 43-50.
(Wei Xiaohang, Yu Chongchong, Tian Changli, et al.Internet CTR Prediction Model on Big Data Platform[J]. Computer Engineering and Design, 2017, 38(9): 2504-2508.)
doi: 10.16208/j.issn1000-7024.2017.09.038
(Gu Zhiyu, Qin Tao, Wang Bin.A Survey of Conversion-based Internet Advertising Model[J]. Journal of Chinese Information Processing, 2014, 28(2): 151-158.)
doi: 10.3969/j.issn.1003-0077.2014.02.022
[14]
吴英. 基于贝叶斯方法的网络广告预测模型研究[D]. 南宁: 广西大学, 2016.
[14]
(Wu Ying.Online Advertising Prediction Model Based on Bayesian Method[D]. Nanning: Guangxi University, 2016.)
[15]
纪文迪. 时间敏感的转化率预测和归因分析[D]. 上海: 华东师范大学, 2017.
[15]
(Ji Wendi.Time-aware Conversion Prediction and Attribution Analysis[D]. Shanghai: East China Normal University, 2017.)
(Hu Qiongfang.Mobile Advertising Pricing Model Researching of Mobile Internet Era——With Tencent Micro-Channel Public Account Ads[D]. Beijing: Beijing University of Chemical Technology, 2015.)
[17]
Jiang Z, Gao S, Dai W.Research on CTR Prediction for Contextual Advertising Based on Deep Architecture Model[J]. Control Engineering & Applied Informatics, 2016, 18(1): 11-19.
(Pan Shumin, Yan Na, Xie Jinkui.Study on Advertising Click-through Rate Prediction Based on User Similarity and Feature Differentiation[J]. Computer Science, 2017, 44(2): 283-289.)
(Wang Yu, Liu Dongsu.Collaborative Filtering Algorithm Based on Bi-clustering and User Attribution Extraction[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(8): 852-858.)
(Shen Jinglei, Yu Huiqun, Fan Guisheng, et al.Design and Implementation of Recommender System Based on Random Forest Algorithm[J]. Computer Science, 2017, 44(11): 164-167.)
(Tian Changli, Zhang Xun, Pan Bo, et al.Research and Implementation of Feature Extraction Methods on Internet CTR Prediction Model[J]. Application Research of Computers, 2017, 34(2): 334-338.)
doi: 10.3969/j.issn.1001-3695.2017.02.003
(Deng Shengxiong, Luo Jiangtao, Liu Yong, et al.Classification Model Based on Ensemble Random Forests[J]. Application Research of Computers, 2015, 32(6): 1621-1624.)
doi: 10.3969/j.issn.1001-3695.2015.06.005
[23]
Zhu J, Shan Y, Mao J C, et al.Deep Embedding Forest: Forest-based Serving with Deep Embedding Features[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 1703-1711.
(Sun Yong, Tan Wen’an, Xie Na, et al.Online Learning Approach for Performance Prediction in Large-Scale Service Computing[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(12): 1922-1930.)
[25]
Grünwald P, De Rooij S.Asymptotic Log-Loss of Prequential Maximum Likelihood Codes[C]// Proceedings of the 18th Annual Conference on Learning Theory. 2005:652-667.
[26]
Lobo J M, Jiménez-Valverde A, Real R.AUC: A Misleading Measure of the Performance of Predictive Distribution Models[J]. Global Ecology & Biogeography, 2008, 17(2): 145-151.
doi: 10.1111/j.1466-8238.2007.00358.x
[27]
王敏, 何炬林, 高作汉. 统计与分析预测理论[M]. 武汉: 武汉大学出版社, 2014.
[27]
(Wang Min, He Julin, Gao Zuohan.Statistical and Analytical Prediction Theory[M]. Wuhan: Wuhan University Press, 2014.)
