Video data recommendation method and device

Abstract

The embodiment of the invention provides a video data recommendation method and device, and the method comprises the steps of monitoring a video data recommendation request comprising the identification information of a target user; acquiring video sequence data of the target user according to the identification information; inputting the video sequence data into a video recommendation model to output a video recommendation result; obtaining at least one corresponding second video according to the similarity of each first video in the video recommendation result; and inserting the second videointo the video recommendation result to obtain a new video recommendation result. The embodiment of the invention does not depend on labels, dimensions and the like of the videos, does not need a large number of videos, is suitable for the videos without the conditions such as labels, dimensions and the like, such as training videos, and improves the recommendation accuracy of the videos such asthe training videos. According to the invention, the new video with high similarity can be inserted into the video recommendation result, the purpose of recommending both the old video and the new video is achieved, and the recommendation rate of the new video is improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method and device for recommending video data. Background technique [0002] With the development of deep learning and big data technology, video recommendation schemes are becoming more and more intelligent. However, the recommendation model with better results usually needs to rely on more video data and more video tags. Correspondingly, it needs to obtain more Customer privacy comes at a higher cost. [0003] In many video recommendation scenarios (such as training video recommendation), the dimensionality of video data is not high, the video data has no labels, and the amount of video data is relatively small. If the traditional video recommendation scheme is used to recommend training videos, it will cause Problems with low accuracy of recommendations. Contents of the invention [0004] In view of the above problems, the embodiments of the present applicati...

AI Technical Summary

Problems solved by technology

[0002] With the development of deep learning and big data technology, video recommendation schemes are becoming more and more intelligent. However, the recommendation model with better results usually needs to rely on more video data and more video tags. Correspondingly, it needs to obtain more Customer privacy, at a higher cost

[0003] In many video recommendation scenarios (such as training video recommendation), the dimensionality of video data is not high, the video data has no labels, and the amount of video data is relatively small. If the traditional video recommendation scheme is used to recommend training videos, it will cause Problems with low recommendation accuracy

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