Video type acquisition method and device based on multiple models, and electronic equipment

Abstract

The invention relates to the field of classification models, and discloses a video type acquisition method based on multiple models, which comprises the following steps: acquiring a behavior feature set, and classifying the behavior feature set to obtain a first behavior feature set and a second behavior feature set; inputting a first behavior feature set and a second behavior feature set into the improved classification model, selecting a first feature subset from the first behavior feature set and the second behavior feature set by using a feature distribution layer, and sending the first feature subset to a first classification sub-model, performing click class classification on a first video playing set corresponding to the first feature subset by using a first classification sub-model to obtain a first type quantity set and a second type quantity set; and performing weight calculation on the first type quantity set and the second type quantity set, and selecting video types interested by the target user from a weight calculation result. According to the invention, the video type in which the user is interested can be quickly judged, so that the accuracy of pushing the video type in which the user is interested is improved.

Description

technical field [0001] The present invention relates to the field of classification models, in particular to a multi-model-based video type acquisition method, device, electronic equipment and computer-readable storage medium. Background technique [0002] With the continuous development of communication technology, the threshold for video playback is gradually lowered, and short videos have gradually become the mainstream of entertainment in the era. Pushing interesting videos to target users has become the main way for major mainstream media to attract users. [0003] At present, the method of users' likes is mainly used to judge the type of videos that users like, but sometimes there are problems such as "the user forgot to like" or "the user accidentally likes", which leads to the inability to obtain more and more accurate data volume, resulting in The prediction results obtained by the prediction model of the push system are not accurate enough, which also makes it impo...

AI Technical Summary

Problems solved by technology

[0003] At present, the method of users' likes is mainly used to judge the type of videos that users like, but sometimes there are problems such as "the user forgot to like" or "the user accidentally likes", which leads to the inability to obtain more and more accurate data volume, resulting in The prediction results obtained by the prediction model of the push system are not accurate enough, which also makes it impossible to accurately obtain the user's interested videos, which in turn leads to inaccurate video push

Images