Video heat prediction method based on deep belief networks and system thereof

A technology of deep belief network and prediction method, applied in the field of video popularity prediction method and system based on deep belief network, can solve the problems of low accuracy and reliability of video popularity prediction, achieve reliable prediction, improve accuracy and reliability sexual effect

Active Publication Date: 2016-06-01
SHENZHEN UNIV
View PDF10 Cites 35 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the object of the present invention is to provide a video popularity prediction method and system based on a deep belief network, aiming at solving the problem of low accuracy and reliability of video popularity prediction in the prior art

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Video heat prediction method based on deep belief networks and system thereof
  • Video heat prediction method based on deep belief networks and system thereof
  • Video heat prediction method based on deep belief networks and system thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0028] The specific embodiment of the present invention provides a video popularity prediction method based on a deep belief network, which mainly includes the following steps:

[0029] S11. Select input variables according to video features and normalize and quantify the impact factors to preprocess the training data;

[0030] S12. Determine the reconstruction dimension of the single-layer restricted Boltzmann machine according to the selected input variable and feature reconstruction error, and form a deep belief through stacking of multi-layer restricted Boltzmann machine and BP neural network ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a video heat prediction method based on deep belief networks. The method comprises following steps: selecting input variables according to video features; normalizing and quantifying influence factors, thus preprocessing training data; determining a single-layer restricted Boltzmann machine reconstitution dimension according to the selected input variable and feature reconstruction errors; forming the deep belief networks by multi-layer stacked restricted Boltzmann machines and a BP (Back Propagation) neural network; adjusting the deep belief networks through a global learning algorithm, thus obtaining an optimum video prediction model; placing to-be-tested video test data in the optimum video prediction model for heat prediction analysis and watching quantity prediction analysis. The invention also provides a video heat prediction system based on the deep belief networks. According to the method and the system of the invention, an online video prediction model based on the deep belief networks is provided; the deep neural network is applied in the online video prediction field; and the prediction accuracy and reliability can be improved.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a video popularity prediction method and system based on a deep belief network. Background technique [0002] Video-on-demand volume prediction plays an important role in the field of Internet data mining. Videos with high volume of on-demand videos (especially movies and TV dramas) can increase the volume of advertisements. Predicting the volume of video-on-demand in advance has a wide range of advertising business expansion. application. [0003] At present, the forecasting of video-on-demand resources generally adopts the prediction method based on historical on-demand data or artificial methods. The prediction method based on historical on-demand data needs to be predicted after the video is broadcast for a period of time, and cannot be predicted before the video goes online. For the forecasting of on-demand volume, the prediction based on artificial methods relies heav...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H04N21/258G06F17/30
CPCG06F16/7867H04N21/258
Inventor 陈亮张俊池王娜李霞
Owner SHENZHEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap