Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 557-563.doi: 10.11896/j.issn.1002-137X.2016.11A.126

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Random Forest Based Telco Out-calling Recommendation System

ZHU Yi-jian, ZHANG Zheng-qing, HUANG Yi-qing, BAI Rui-rui and YAN Jian-feng   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Out-calling recommendation is widely used in recommending products and services to customers by telecommunication (telco) operators.In this paper,we developed an automatic out-calling recommendation system which relies on telco big data.This system uses data-mining methods to extract customer behaviors and machine-learning algorithm to predict the acceptance probabilities when customers are recommended to certain products.Different from most recommendation systems which use matrix factorization (MF),sparse features classification,neural network and etc,this paper used random forest,not only because the algorithm is easy to be parallel implemented and has fast training speed,but also the rules from the resulting decision trees are easy to explain.These characteristics make the random forest suitable for telco recommendation system.Our system is implemented on the top of Hadoop,Impala and Spark.Random forest is used as the core algorithm to calculate the acceptance probability when a user is recommended to a product based on user behavior features.Online testing shows that the proposed system can achieve 41% improvement compared with the current deployed random out-calling recommendation method.We also gave the customer behavior analysis according to the feature importance from the outputs of random forest.

Key words: Out-calling,Recommend system,Random forest,Telco operator

[1] Sarwar B,Karypis G,Konstan J,et al.Application of dimensionality reduction in recommender system-a case study[R].Minnesota Univ Minneapolis Dept of Computer Science,2000
[2] Koren Y,Bell R,Volinsky C.Matrix factorization techniques for recommender systems[J].Computer,2009 (8):30-37
[3] Huang Yi-qing,Zhu Fang-zhou,Yuan Ming-xuan,et al.Telcochurn prediction with big data[M].SIGMOD,2015
[4] Yuan Ming-xuan,Deng Ke,Zeng Jia,et al.OceanST:A distributed analytic system for large-scale spatiotemporal mobile broadband data[C]∥VLDB (Demo).2014:1561-1564
[5] Page L,Brin S,Motwani R,et al.The PageRank Citation Ranking:Bringing Order to the Web[R].Stanford InfoLab,1999
[6] Zhu X,Ghahramani Z.Learning from labeled andunlabeled data with label propagation[R].Technical Report CMU-CALD-02-107,Carnegie MellonUniversity,2002
[7] Zeng J,Cheung W K,Liu J.Learning topic modelsby belief propagation[J].IEEE Trans.Pattern Anal.Mach.Intell.,2013,35(5):1121-1134
[8] Rendle S.Scaling factorization machines to relational data[C]∥PVLDB.2013:337-348
[9] Neslin S,Gupta S,Kamakura W A,et al.Defection Detection:Measuring and Understanding the Predictive Accuracy of Customer Churn Models[J].Social Science Electronic Publishing,2006,43(2):204-211
[10] Hadden J,Tiwari A,Roy R,et al.Computer assisted customerchurn management:State-of-the-art and future trends[J].Computers & Operations Research,2007,34(10):2902-2917
[11] Lima E.Domain knowledge integration in data mining using decision tables:case studies in churn prediction[J].Journal of the Operational Research Society,2009,60(8):1096-1106(11)
[12] Verbeke W,Martens D,Mues C,et al.Building comprehensible customer churn prediction models with advanced rule induction techniques.[J].Expert Systems with Applications,2011,38(3):2354-2364
[13] Jinbo S,Xiu L,Wenhuang L.The Application ofAdaBoost inCustomer Churn Prediction[C]∥2007 International Conference on Service Systems and Service Management.IEEE,2007:1-6
[14] Lemmens A,Croux C.Bagging and boosting classification trees to predict churn[J].Journal of Marketing Research,2006,43(2):276-286
[15] Datta P,Masand B R,Mani D,et al.Automated Cellular Modeling and Prediction on a Large Scale[J].Artificial Intelligence Review,2000,14(6):485-502
[16] Hung S,Yen D C,Wang H.Applying data mining to telecomchurn management.[J].Expert Systems with Applications,2006,31:515-524
[17] Burez J,Van den Poel D.Handling class imbalance in customer churn prediction[J].Dirk Van den Poel,2008,36(3):4626-4636
[18] Davis J,Goadrich M.The Relationship Between Precision-Recall and ROC Curves[C]∥ICML ’06:Proceedings of the 23rd International Conference on Machine Learning.2006
[19] 方匡南,吴见彬,朱建平,等.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38

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