Mobile data traffic package recommendation algorithm based on user historical data

Abstract

The invention provides a mobile data traffic package recommendation algorithm based on user historical data according to data mining analysis technology. The mobile data traffic package recommendation algorithm comprises the following steps of: 1) a target user finding period comprising the processes of a, acquiring a processed generated data set which comprises a training set and a prediction set, b, executing a random forest classification algorithm for finding a latent data traffic package improving user as a target user, and c, ending; 2), a data traffic package recommendation period comprising the process of a, acquiring a processed generated prediction set, b, executing a K-means clustering algorithm for obtaining a slightly similar user cluster, c, obtaining the target user obtained in the process 1)-b, d, executing a TopN recommendation algorithm on the target user in a same cluster according to a similarity function of the user, and e, ending. The mobile data traffic package recommendation algorithm is used for finding the latent user with a latent data traffic improvement requirement according to data mining technology and executing a recommended plan on the user. Compared with a traditional method, the mobile data traffic package recommendation algorithm has advantages of higher accuracy, higher efficiency, simple realization, low cost, etc.

Description

technical field [0001] The invention relates to a traffic package recommendation algorithm, which uses user historical consumption data and data mining technology to find potential promotion users, and uses a collaborative filtering recommendation algorithm to recommend a suitable traffic package to them. Background technique [0002] With the advent of the mobile Internet era, users' lifestyles have changed, which has shifted the operating focus of telecom operators. Traffic services will gradually replace traditional voice services and become the focus of competition and profit points in the telecom industry. Use precision marketing to accurately predict users who may potentially upgrade data packages, and recommend suitable data packages to users. Precision marketing can not only stimulate the use of user traffic, but also improve user satisfaction. [0003] The traditional traffic package recommendation is for all users and the recommendation is relatively random, which...

AI Technical Summary

Problems solved by technology

[0003] The traditional data package recommendation is for all users and the recommendation is relatively random, which makes the recommendation effect not obvious and is disgusted by many users

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