Video recommending method and device

Abstract

The embodiment of the invention provides a video recommending method and device. The problems that in the prior art, appropriate videos cannot be recommended for a user according to the requirement and interests of the user, and the recommending accuracy is not high are solved. The method comprises the steps that video attribute information is obtained, wherein the video attribute information comprises video tags and video tag types; according to the video attribute information and a preset video tag type weight, a weight score between a target video and other videos is calculated, wherein the video tag type weight reflects the priorities of the video tag types; according to the weight score between the target video and the other videos, a video recommending list is generated for recommending.

Description

technical field [0001] The present invention relates to the technical field of recommendation, in particular to a video recommendation method and device. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity, and the recommendation system came into being. The recommendation system finds interesting videos for users beyond their known scope and expands their viewing experience. [0003] In the existing video recommendation system, one is to calculate the similarity between videos based on video tags, that is, to calculate the similarity between videos according to whether a certain number of inherent tags match, and to generate a recommendation list for users according to the similarity between videos. Watch the video. However, there are two problems in the method of calculating the similarity between videos based on video tags in the p...

AI Technical Summary

Problems solved by technology

However, there are two problems in the method of calculating the similarity between videos based on video tags in the prior art: first, the tags have problems such as information overload, disorder, and garbage tags, which will affect the accuracy and personality of the recommendation system Second, for video recommendation, the importance of each inherent label to the user is different. If they are all treated equally, the accuracy of the recommendation will not be high and the user experience will be affected.

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