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LBSN supernetwork link prediction method based on time-space relationship

A prediction method and technology of time-space relationship, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as failure to use time information

Active Publication Date: 2018-03-09
CHONGQING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, the existing hypernetwork-based methods do not make use of time information, so there is still a lot of room for improvement in their accuracy.

Method used

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  • LBSN supernetwork link prediction method based on time-space relationship
  • LBSN supernetwork link prediction method based on time-space relationship
  • LBSN supernetwork link prediction method based on time-space relationship

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Embodiment Construction

[0082] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0083] Such as figure 1 , figure 2 As shown, it includes four modules: acquiring data module, constructing hypernetwork model, defining and quantifying network edge weights, and prediction method of LBSN hypernetwork link based on spatiotemporal relationship.

[0084] The detailed implementation process of the present invention is specifically described below.

[0085] S1: Get the data source. The acquired data is yelp data, which is an open data set of yelp website. The obtained data content mainly includes the friend relationship between users, user comments and ratings on the store, the longitude and latitude of the store, and the category it belongs to, etc.

[0086] S2: Build a hypernetwork model. Because in location-based social networks, the establishment of links is affected by many factors, such as time factors, location fact...

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Abstract

The invention relates to an LBSN supernetwork link prediction method based on time-space relationship and belongs to the field of data mining. The method comprises the following steps: S1, acquiring adata source; S2, constructing a supernetwork model; S3, defining and quantizing a supernetwork edge weight value; and S4, based on the model, firstly constructing multiple types of weighting hyperedge structures, mining different semantic relations among users by virtue of different structures, finally training model parameters by virtue of a gradient descent method, and then predicting link relations in network. The method provided by the invention has the advantages that multiple incidence relations among nodes can be effectively mined, the sparsity problem in the network can be solved, anti-noise property and stability of the model also can be improved, and predictive accuracy of the model is also greatly improved.

Description

technical field [0001] The invention belongs to the field of data mining and relates to a method for predicting LBSN hypernetwork links based on spatio-temporal relations. Background technique [0002] With the continuous development of computer information technology and the rapid popularization of the Internet, online social platforms have become an indispensable part of people's lives. Through this platform, people can establish their own friend relationship network and conduct instant communication and interaction with friends. It has greatly facilitated people's life, especially in recent years, the emergence of location-based social network (Location-Based Social Network, referred to as LBSN) has made some location-based services highly praised by a large number of users in a short period of time and gained great popularity. success. In the LBSN, the user can check-in at the location he has been to, and share his check-in location with his friends. This check-in behav...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06Q50/01G06F16/9535G06F16/9537G06F16/9558
Inventor 胡敏陈元会黄宏程
Owner CHONGQING UNIV OF POSTS & TELECOMM
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