Method for representing crowd movement patterns through context-dependent graph embedding

A technology of moving patterns and embedded representations, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as lack of labeled training data

Inactive Publication Date: 2020-02-21
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Claims
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Problems solved by technology

(3) Lack of labe

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  • Method for representing crowd movement patterns through context-dependent graph embedding
  • Method for representing crowd movement patterns through context-dependent graph embedding
  • Method for representing crowd movement patterns through context-dependent graph embedding

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Embodiment

[0053] The method provided by this implementation to represent crowd movement patterns through context-sensitive graph embeddings can be used for such datasets containing user check-in records. The real-world LBSN dataset as shown in Table 1, with Gowalla ( http: / / snap.stanford.edu / data / loc-gowalla.html acquisition) as an example to experiment.

[0054] Table 1: Relevant information of experimental data of the present invention

[0055]

[0056] |u| indicates the number of users;

[0057] |D L | / |D U |respectively represent the number of sub-trajectories used for training and testing;

[0058] |L| represents the number of different check-in points in the dataset;

[0059] Represents the average length of the trajectory before it is divided;

[0060] T r Represents the range of trajectory lengths in the dataset;

[0061] New York represents the data that the area in the dataset Foursquare is New York;

[0062] Tokyo represents the data whose region is Tokyo in...

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Abstract

The invention provides a new method for representing crowd movement through a context-related graph embedding model. The method comprises the following steps: firstly, generating a fully-connected context correlation graph according to a historical track of a user; applying a word embedding technology to a graph model for the first time, learning sign-in point vector representation fused with context semantic information, then obtaining vector representation of each track through a recurrent neural network, and then introducing a reinforcement learning method to find out a current track generator. In the training process of the whole model, the track with the label and the track without the label are used for training synchronously, meanwhile, a strategy gradient method in adversarial learning is used for updating model parameters, and the model performance is further enhanced.

Description

technical field [0001] The invention belongs to the field of neural networks in machine learning, and is a method based on deep learning. The method applies word embedding technology to a graph model for the first time, and learns the vector representation of check-in points that integrates contextual semantic information, and then passes through a cyclic neural network. (Recurrent Neural Network, RNN) obtains the vector representation of each trajectory, and then uses reinforcement learning to extract the user's implicit movement patterns from the labeled and unlabeled trajectory data to find the user who generated the current trajectory. Background technique [0002] In Location-Based Social Network (LBSN) applications based on geographic location information, such as personalized location recommendation, travel planning, and crowd flow prediction, it is an important and complex task to learn crowd mobility rules. This task has a very wide range of application scenarios, f...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06K9/62G06F40/30G06F16/29G06F16/9537G06Q50/00
CPCG06N3/084G06F16/29G06F16/9537G06Q50/01G06N3/045G06F18/241
Inventor 刘芳钟婷周帆
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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