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

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