A Comparative Study on Contract Recommendation Model: Using Macao Mobile Phone Datasets

Abstract

Bordering on the Mainland of China, Macao is a city with diverse population and is also a world-famous gambling city. The residents living in Macao are not only localresidents, but a large-scale of residents are from the Mainland of China. There are diversified living habits among different resident groups. Some of them live and work in Macao most of the time, while others cross the border between Macao and the Mainland of China frequently. These diversities result in different demands for mobile phone contract services. Machine learning and data mining are powerful tools used by telecom companies to monitor the behavior of their customers. This paper aims to build a contract service recommendation model suitable for Macao telecom companies, which can accurately recommend the best fit contract services according to call data records (CDR). Based on data mining algorithm and large number of comparative experiments, this study conducts a study on variety of factors that have effect on the contract recommendation for obtaining a composition structure of factors that have the greatest impact on contract recommendation accuracy so as to improve the operation efficiency of the model. In addition, this study makes comparisons among five classification algorithms, including Bayesian, logistic, Random trees, Decision tree (C5.0), and KNN (k-nearest neighbor). The results are analyzed by different metrics such as Gain, Lift, ROI (region of interest), Response, and Profile. Experimental results demonstrate that the best classifier is decision trees (C5.0), and the contract service obtained by this method can achieve a recommendation accuracy close to 90%, which successfully reaches the expected goal.