Research On Intelligent Recommendation Model And Application Of Personalized Packages For Users In Telecom Industry

In recent years,telecom operators have introduced a large number of telecom packages to meet the differentiated needs of users.The package design is more and more diversified,the types of packages are continuously enriched and improved.However,many telecom package products launched in the market do not give operators more competitiveness and market advantages.On the contrary,users cannot select products that meet their needs from a large number of packages because of the overload and asymmetry of package information.Therefore,it has become an important research direction in the field of telecommunications to analyze users' consumption habits and behavior rules and recommend personalized packages to users.This paper aims to solve the problem of intelligent recommendation of telecommunication users' packages based on large-scale data and data mining.This paper proposes an adaptive multi-layer classification(AMC)strategy and XGBoost algorithm based on the large-scale real data of more than 740,000 telecom users' historical consumption,and builds an intelligent recommendation integration model(AMC-XGBoost)of telecom customer package.Firstly,the model preprocesses the data and excavate the behavior characteristics and influencing factors of customer selecting packages based on feature selection method of random forest.Secondly,data tests are performed based on XGBoost algorithm and other classification algorithms,and performances are selected and compared.Then,the idea of multi-level classification is adopted to separate different levels of package categories by adaptive adjustment threshold,and the integrated model is constructed by XGBoost algorithm.Furthermore,considering the potential characteristics of data mining user behavior,this paper analyzes the influencing factors of user behavior on telecom package recommendation and gives feasible suggestions from the four dimensions of package contract characteristics,traffic characteristics,cost characteristics and call behavior characteristics.The research results show that the accuracy of the prediction result of the integrated model is 92%,the F1 value is 93%,and the recall rate is 89%.The standard deviation of each group of indicators is less than 0.05,indicating that the model performance is stable and the prediction effect is good.Improved accuracy of package category recommendations.The evaluation results show that the classification performance of the integrated model is better than other single classification algorithms,which verifies the rationality and effectiveness of the model.In the research,it was found that telecom users' cost information and traffic information characteristics are ranked first in the index importance,which is a very important type of indicator characteristics in the package recommendation.At the same time,the remaining contract length characteristics and superset duration ratio characteristics of the package are also key characteristic indicators for users.Relatively,various complicated value-added services and other low value package services in the package will not affect users' choice of packages too much.Therefore,focusing on the above key index characteristics can provide valuable decision support for the subsequent package design and recommendation strategy of the telecom enterprises.