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Mobile data traffic package recommendation algorithm based on user historical data

A technology of traffic package and recommendation algorithm, applied in data processing applications, computing, computer parts, etc., can solve problems such as insignificant recommendation effect, random traffic package recommendation, and user disgust.

Inactive Publication Date: 2016-03-09
NANJING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

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  • Mobile data traffic package recommendation algorithm based on user historical data
  • Mobile data traffic package recommendation algorithm based on user historical data
  • Mobile data traffic package recommendation algorithm based on user historical data

Examples

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Embodiment Construction

[0054] In order to better understand the technical content of the present invention, specific examples are given and described as follows in conjunction with the accompanying drawings.

[0055] Such as figure 1 As shown, the mobile user data package recommendation algorithm is to generate training data sets and prediction training sets through data preprocessing, and then use the random forest classification algorithm to mine potential promotion users as target users, and finally use the K-means clustering algorithm to obtain similar user clusters , that is, the user's neighbors, and use the collaborative filtering algorithm on the basis of the user's neighbors to obtain the user's scoring matrix for the item, and get the TopN package recommendation list.

[0056] Random forest classification algorithm mining potential users and K-means combined with collaborative filtering package recommendation algorithm are the main steps of the invention. The idea of ​​the invention is to ...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06K9/62
CPCG06Q30/0202G06F18/24323
Inventor 吴骏王陆霞戴恒宇蔡阳刘洋王崇骏
Owner NANJING UNIV
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