Short video recommendation model based on short video multi-modal features

A short video, multimodal technology, applied in the field of video analysis

Pending Publication Date: 2020-08-11
CENT SOUTH UNIV
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AI Technical Summary

Problems solved by technology

[0005] The present invention provides a short video recommendation model based on the multimodal features of short videos, and its purpose is to solve the problem of short video recommendation based on the multimodal features of short videos

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  • Short video recommendation model based on short video multi-modal features
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  • Short video recommendation model based on short video multi-modal features

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Embodiment Construction

[0107] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0108] Aiming at the existing problem of short video recommendation based on the multimodal features of the short video, the present invention provides a short video recommendation model based on the multimodal features of the short video.

[0109] Such as Figure 1 to Figure 6 As shown, the embodiment of the present invention provides a short video recommendation model based on short video multi-modal features, including: Step 1, using the TF-IDF method for feature extraction of short video title features, using the PCA dimensionality reduction algorithm Reduce the short video title feature vector dimension to k dimension; step 2, extract the 128-dimensional original feature of the short video content, use the PCA dimensionality reduction algorithm to r...

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Abstract

The invention provides a short video recommendation model based on short video multi-modal features, and the model comprises the steps: 1, carrying out the feature extraction of short video title features through employing a TF-IDF method, and reducing the dimension of a short video title feature vector to k dimension through employing a PCA dimension reduction algorithm; 2, extracting 128-dimensional original features of the short video content, and adopting a PCA dimension reduction algorithm to reduce the dimension of the feature vector of the short video content to k dimension; 3, extracting 128-dimensional original features of the short video background music, and reducing the dimension of the feature vector of the short video background music to k dimensions through a PCA dimension reduction algorithm. According to the invention, the influence effects of the feature data of different modes on the user behaviors generated by the user are considered to be different; and learning the influence proportion of different modal data of the short video on the user by using a hidden Markov model, and mapping the multi-modal features of the short video to a unified vector space for fusion based on the influence proportion to obtain short video feature data represented by the multi-modal data features.

Description

technical field [0001] The invention relates to the technical field of video analysis, in particular to a short video recommendation model based on multimodal features of the short video. Background technique [0002] With the rapid development of society and the acceleration of people's life rhythm, almost everyone is under a certain amount of pressure in work or life, and it is difficult for most people to have a lot of leisure time to entertain and relax due to the burden of life. With the rapid development of the mobile Internet and the emergence of short video apps such as Douyin and Kuaishou, many people like to browse and watch some short videos in their limited and fragmented free time to relax themselves, but due to the low threshold of short video apps , almost everyone can publish their own works on it, resulting in a large number of short video works on the app and mixed content. It is difficult for people to quickly find the short videos they are interested in f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00G06F16/9535G06F16/735
CPCG06N20/00G06F16/735G06F16/9535G06F18/213G06F18/214
Inventor 邓晓衡张桦林黄文俊赵敏
Owner CENT SOUTH UNIV
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