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Method of establishing user relation prediction model and predicting user dynamic relation

A technology for predicting models and establishing methods, which is applied in the field of network communication and can solve problems such as poor nonlinear classification effects

Active Publication Date: 2018-05-01
INST OF ACOUSTICS CHINESE ACAD OF SCI +1
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Problems solved by technology

Although the method has good linear classification ability, the non-linear classification effect is poor

Method used

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  • Method of establishing user relation prediction model and predicting user dynamic relation
  • Method of establishing user relation prediction model and predicting user dynamic relation
  • Method of establishing user relation prediction model and predicting user dynamic relation

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Embodiment Construction

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0061] Such as figure 1 As shown, a method for establishing a user relationship prediction model, the method includes:

[0062] Step S1) Obtain two sub-networks of user relationship from the original social relationship network through random walk sampling;

[0063] Specifically, in a specific implementation manner, taking the 2012KDD Cup dataset as an example, the dataset includes a user_sns.txt file, which is a user-following relational data file.

[0064] Each line in the user follow...

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Abstract

The invention discloses a method of establishing a user relation prediction model, comprising the steps of S1) obtaining a subnetwork of two user relations from the original social relation network through random sampling; S2) respectively extracting four topology features including the common friends number, common friends clustering coefficient, friends clustering coefficient and the shortest path distance of a user two-tuples connected to each edge in the subnetwork; S3) establishing the user relation prediction model, which has a feedforward neural network structure; S4) obtaining an optimal individual by means of genetic algorithm based on a training set and the established user relation prediction model, wherein the individual is a well-trained user relation prediction model. In addition, the invention provides a method of predicting the user dynamic relation, which can predict the dynamic change of user relation. The prediction method is advantageous in that the method is not restricted by the shortest path distance D when predicting the user relation; the accuracy of predicting the user relation and the analysis capability of weak relation can be improved.

Description

technical field [0001] The invention relates to the technical field of network communication, in particular to the establishment of a user relationship prediction model and a method for predicting user dynamic relationships. Background technique [0002] With the rapid development of social networks, applications based on social networks have profoundly changed human interaction behavior and the way information is disseminated. Users obtain and disseminate the latest information through social media, find people with the same interests or nearby, maintain current friendships, share shopping experience, recommend video information, etc. According to Nielsen's 2014 survey report, the number of active social media users reached 1.86 billion, and the average time each social media user spends on social media is 1.5 hours per day. Nielsen's survey results show that 90% of users trust their friends' recommendations, and 70% of users trust other users' comments on advertised produ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/00
CPCG06Q10/04G06Q50/01
Inventor 盛益强李南星刘学
Owner INST OF ACOUSTICS CHINESE ACAD OF SCI
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