Prediction Method of Communication Network User Calling Object Based on Representation Learning and Behavior Features

A forecasting method and communication network technology, which is applied in the field of communication network user behavior analysis, can solve the problems of limited capacity of forecasting methods, difficulty in effectively dealing with traditional methods, lack of ready-made technical solutions and in-depth research on forecasting problems, etc.

Active Publication Date: 2021-04-30
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0003] However, there are many difficulties in predicting the target user's next communication object in the large-scale user call historical behavior data: First, the diversified services of telecom operators and the rapid growth of user data have produced massive user data, often a market There are tens of millions of users in the communication data within the level range, and the large amount of calculation makes it difficult to effectively deal with traditional methods; in addition, users are not independent individuals in the communication network, but are associated and form a communication network, and The user's call history contains information such as communication priority, and the communication object prediction method that only considers the statistical characteristics of the user's call history record or the communication object prediction method that only considers the user's node similarity in the communication network has limited capabilities; in addition, the existing The research on user behavior in the communication network mainly focuses on the analysis of the overall traffic change of the communication network, the identification of counterfeit numbers, and the detection of fraudulent calls. There is a lack of ready-made technical solutions and in-depth research on the prediction of the next communication object of the user

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  • Prediction Method of Communication Network User Calling Object Based on Representation Learning and Behavior Features
  • Prediction Method of Communication Network User Calling Object Based on Representation Learning and Behavior Features
  • Prediction Method of Communication Network User Calling Object Based on Representation Learning and Behavior Features

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Embodiment 1

[0057]This embodiment provides a communication network user call object prediction method based on the characteristics of learning and behavioral characteristics, such asfigure 1As shown, including the following steps:

[0058]Step 1: N (0,0.05) in communication nodes in the communication network2The normal distribution random initialization generates the initial vector of each communication nodeGet the initial vector collection of communication nodesThe initial vector dimensions of each communication node are 100 dimensions; where | V | is the total number of communication nodes in the communication network;

[0059]Step 2: Depending on the order of the communication node in each user call history in the communication network, the order of the call sequence network G = {V, E}; where V is the communication node collection, E is a collection of communication nodes between the V-V., ie the user call communication nodeAfter that, call another communication nodeThen, there will be contacts,,;...

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Abstract

The invention provides a communication network user call object prediction method based on representation learning and behavior characteristics, which belongs to the technical field of communication network user behavior analysis, including: randomly generating the initial vector of each communication node in the communication network, constructing a call sequence network; Extract triplet sets from historical records, construct and train a graph representation learning model stacked by LSTM cyclic neural network and bilinear layer, and obtain updated communication node vectors of all communication nodes; construct and train by parallel communication duration‑ Position weighting layer and bidirectional long-term short-term memory neural network, stacking feed-forward neural network and bilinear layer call object prediction model; the historical call records of users to be predicted are based on the updated communication node vector and the call object prediction model after training, Fulfill predictions. The invention is based on the analysis of user call history records, and realizes prediction according to communication node topology information, communication sequence information and long-term and short-term behavior characteristics of users.

Description

Technical field[0001]The present invention belongs to the field of communication network user behavior analysis, and specifically, the present invention relates to a communication network user call object prediction method based on representation and behavioral characteristics.Background technique[0002]With the update of information technology, the mobile communication tools in recent years have become rapidly popular, and greatly facilitating people's lives and producing massive user communication behavior history data. However, the convenient communication tool has also become the linkage and organization of the criminal gang. Due to the hiddenness of the criminal gang organizes and the lag of the law enforcement personnel, the prediction of the next communication target in communication network has become a very important and Valuable questions, can effectively assist relevant law enforcement departments to accurately predict the future possible communication objects of target us...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/26G06Q50/30G06N3/04G06N3/08
CPCG06Q10/04G06Q50/265G06Q50/30G06N3/084G06N3/048G06N3/044G06N3/045
Inventor 刘峤蓝天曾义夫代婷婷宋明慧周乐孙建强曾维智吴祖峰
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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