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Federal learning-based video distribution method and system

A video and federation technology, applied in the field of data processing, can solve the problems of low pertinence, low click-through rate of users on video applications, and unsatisfactory recall effect.

Pending Publication Date: 2022-04-08
江苏号百科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the prior art, videos are pushed to users indiscriminately, with low pertinence, low click-through rate of users for video applications, and unsatisfactory recall effect

Method used

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  • Federal learning-based video distribution method and system
  • Federal learning-based video distribution method and system
  • Federal learning-based video distribution method and system

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Embodiment Construction

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0068] Such as Figure 4 As shown, the present invention selects a user group of 4K IPTV set-top boxes with high configuration and strong computing power in a certain area, selects a content distribution server in this area as the aggregation server in the federated learning framework, and uses IPTV video content as the material library.

[0069] As mentioned in the background technology, almost all users currently use an indiscriminate video push method to push vide...

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Abstract

The invention provides a federated learning-based video distribution method and system. The method comprises the following steps of: calculating the accepted degree of a target video in a mobile edge computing server within target time; calculating the profit generated by the target video within the target time; calculating the average distribution profit of the target video in the target time; deleting the target videos of which the average distribution profit is lower than the target profit; constructing a video interest matrix; acquiring user data; training a convolutional neural network model according to the user data; updating parameters of the convolutional neural network model; uploading the updated convolutional neural network model parameters to a mobile edge computing server; calculating the average profit of each type of video; and deleting the video with the average profit attenuation degree higher than the target attenuation degree. According to the method and the device, the problems of low pertinence, low click rate of the user to the video application and non-ideal recall effect in the prior art of indiscriminately pushing the video to the user are solved.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a video distribution method and system based on federated learning. Background technique [0002] In the field of video push, a network architecture that caches video content in a mobile edge computing server (Mobile Edge Computing, MEC) is proposed, which can shorten the video content access delay and video retrieval times. At present, there are mainly two content distribution strategies: one is based on user needs and passive content distribution strategy; the other is that before the user initiates a request for video content, the video content provider sends a part of the video content that the user is about to request or most wants to watch Policies cached in advance in the cache of the mobile edge server. [0003] At present, almost all users use an indiscriminate video push method to push videos to each user, and it is difficult to achieve targeted pus...

Claims

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Application Information

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IPC IPC(8): H04N21/442G06N3/04G06N3/08G06N20/20H04N21/45H04N21/482
Inventor 汪伟亚包琳徐姗袁梦婷
Owner 江苏号百科技有限公司
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