Media asset recommendation method, display device and server

Abstract

The embodiment provides a media asset recommendation method, a display device and a server, the server receives a user behavior log of a target user sent by the display device, inputs the user behavior log into a recommendation model, and outputs a recommended media asset list. And clustering the target users into two target groups by using the user behavior logs. And calculating a popularity difference value of the two target groups, determining the group with the maximum popularity difference value as an exposure-deviation-free group, and constructing a confrontation model by using a user behavior log of the exposure-deviation-free group. And inputting the user behavior log of the target user, the recommended media asset list and the unpopular media asset data into the constructed confrontation model, and outputting a confrontation media asset list. And finally, outputting an exposure deviation removing media asset list according to the recommended media asset list and the adversarial media asset list. Therefore, by utilizing model confrontation, the influence of non-user interest behaviors caused by hot media assets and passive exposure media assets can be reduced in the aspect of media asset recommendation, and the effect of removing exposure deviation is achieved, so that the watching experience of the user is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular, to a media asset recommendation method, display device and server. Background technique [0002] With the continuous development of Internet technology, users are no longer limited to watching videos on TV, but can also search for videos of interest to watch through the Internet. In addition, the Internet can also automatically recommend videos to users, which is convenient for users to choose. [0003] The current Internet recommends videos to users, which may be based on user clicks, recommending videos that the user is interested in to the user. Or, recommend videos with high hot topics to users. [0004] However, based on how users click on recommended videos, it may not really reflect user interests. For example, the passively exposed columns on the startup homepage will be clicked excessively by users, and the clicked videos may not be the rea...

AI Technical Summary

Problems solved by technology

[0005] This application provides a media asset recommendation method, display device, and server, which are used to solve the problem of recommending videos based on user clicks, which may not truly reflect user interests, and some users may not be interested in videos with high-hot topics.

Recommending videos to users through existing methods will result in poor viewing experience for users.

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