Video pushing method and device based on classification characteristics and electronic equipment

Abstract

The embodiment of the invention provides a video pushing method and device based on classification characteristics and electronic equipment, and belongs to the technical field of data processing, and the method comprises the steps: obtaining one or more target videos; analyzing different types of contents contained in the target video to obtain a content analysis result; classifying the content analysis result through a preset classification model to form classification information corresponding to the target video; and pushing a target video matched with the classification information to a target object. Through the processing scheme disclosed by the invention, the video pushing accuracy is improved.

Description

Technical field [0001] The present disclosure relates to the field of data processing technology, and in particular to a video push method, device and electronic equipment based on classification features. Background technique [0002] With the continuous development of Internet technology, online videos are becoming more abundant. Users watching videos are no longer limited to TV. They can also search for videos of interest through the Internet. After analyzing the user’s video hobbies, the video providing platform can actively provide The user recommends the video, thus facilitating the user to watch the video. In order to grasp the user's behavior habits, it is usually necessary to view the user's history of watching videos, and make video recommendations through a large amount of historical behavior data. [0003] In the existing recommendation system, the learning and training of the recommendation system is mainly dependent on the interaction between the user and the recomme...

AI Technical Summary

Problems solved by technology

[0003] In the existing recommendation system, the learning and training of the recommendation system mainly rely on the interaction between the user and the recommendation information. The recommendation effect depends on the interaction between the user and the user, and the interaction between the user and the recommendation information. , the recommendation system lacks the content understanding of the recommended information itself, resulting in the lack of a complete understanding of the video content in the recommendation process

Especially, when there are new users or new recommendable videos, the recommendation effect is often unsatisfactory.

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