Video recommendation method and system based on deep neural network, and storage medium

A deep neural network and video recommendation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of lack of user scoring mechanism and inability to personalize user video recommendations

Pending Publication Date: 2020-07-07
SHENZHEN TCL NEW-TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In view of the deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide a video recommendation method based on a deep neural network and a storage medium, so as to overcome the lack of a user scoring mechanism when watching online videos in the prior art, which leads to the inability to score according to users. Defects in personalized video recommendation for users

Method used

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  • Video recommendation method and system based on deep neural network, and storage medium
  • Video recommendation method and system based on deep neural network, and storage medium
  • Video recommendation method and system based on deep neural network, and storage medium

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Experimental program
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Embodiment 1

[0038] The first embodiment disclosed by the present invention is a video recommendation method based on a deep neural network, such as figure 1 As shown, the personalized recommendation applied to online video includes the following steps:

[0039] Step S1. Obtain the video data watched by the user, and generate a positive sample data set and a negative sample data set according to the user's watched video data and the video data included in the video library.

[0040] In order to make personalized video recommendations for each user, it is first necessary to obtain the user’s historical viewing video information, and to get the type of video he or she likes from the historical viewing video information, and the situation of the video watched, and according to the historical viewing video information as It makes video recommendations. Therefore, in this step, it is first necessary to obtain video viewing data of multiple users, and use the video viewing data of users as a po...

Embodiment 2

[0059] The second embodiment disclosed by the present invention is a video recommendation system based on a deep neural network, such as image 3 shown, including:

[0060] The training set acquisition module 310 is used to acquire the video data watched by the user, and generate a positive sample data set and a negative sample data set according to the video data watched by the user and the video data included in the video database; its function is as described in step S1.

[0061] The network model training module 320 is used to use the positive sample data set and the negative sample data set to perform autoencoder model training based on the deep neural network to obtain a training network model; its function is as described in step S2.

[0062] The prediction and recommendation module 330 is used to input the video data watched by each user and the video data of the video library into the training network model to obtain a predicted video recommendation list; its function...

Embodiment 3

[0074] The third embodiment disclosed by the present invention is: a storage medium, wherein a control program for video recommendation based on a deep neural network is stored on the storage medium, and the control program for video recommendation based on a deep neural network is executed by a processor The step of implementing the video recommendation method based on the deep neural network described in the item during execution.

[0075] The storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data used or received by the industrial equipment management method, and the like. In addition, the storage medium may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state st...

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PUM

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Abstract

The invention provides a video recommendation method and system based on a deep neural network, and a storage medium. According to the method and system, the watching video data of a user are obtained; a positive sample data set and a negative sample data set are generated according to the watching video data of the user and the video data of a video library; autoencoder model training based on adeep neural network is performed with the positive sample data set and the negative sample data set, so that a training network model can be obtained; the video watching data of each user and the video data of the video library are inputted into the training network model, so that a predicted video recommendation list can be obtained; and video recommendation is performed on each user according tothe predicted recommended video list. According to the method provided by the invention, personalized recommendation under a network television condition is realized, recommendation accuracy is ensured based on the deep network, and convenience is provided for the user to watch videos.

Description

technical field [0001] The present invention relates to the technical field of display control, in particular to a deep neural network-based video recommendation method and a storage medium. Background technique [0002] At present, many websites use recommendation systems to recommend products to users. Recommendation systems can be roughly divided into two categories: contextual recommendations based on browsing information and personalized recommendations based on user history information. Context-based recommendations take into account contextual information such as location, date, and time. Personalized recommendation usually uses collaborative filtering to recommend products to users. In this approach, predictions of a user's interest are based on an analysis of the tastes and preferences of other users in the system, and an implicit inference of "similarity" between the two. The underlying assumption is that when two people have similar tastes, they will have a hig...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/735G06K9/62H04N21/466H04N21/482H04N21/25
CPCH04N21/4666H04N21/4667H04N21/4668H04N21/4826H04N21/251G06F18/241G06F18/214H04N21/466H04N21/25H04N21/482G06F16/735G06F18/00
Inventor 徐永泽赖长明
Owner SHENZHEN TCL NEW-TECH CO LTD
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