Short video click rate prediction method based on collaborative attention mechanism

Abstract

The invention discloses a short video click rate prediction method based on a collaborative attention mechanism. According to the method, based on a click sequence of a user to a short video, the tendency of the user to different aspects of the short video and the importance of the user to different aspects of the short video are mined, and the click rate of the user to a target short video is predicted. The method is mainly composed of three parts. In the first part, a user click sequence and a target short video are mapped to an aspect space and user and short video representation in the aspect space are obtained. And in the second part, after the representation of the user and the short video in the multi-aspect space is obtained, the importance of the user and the short video in different aspects is obtained by using a co-attention mechanism. And in the third part, the click rate of the short video is predicted based on the output results of the first two parts.

Description

technical field [0001] The invention belongs to the technical field of Internet services, and in particular relates to a short video click rate prediction method based on a collaborative attention mechanism. Background technique [0002] Short video is a new type of short video. The shooting of short videos does not require the use of professional equipment or professional skills. Users can easily shoot and upload to the short video platform directly through their mobile phones, so the number of short videos on the short video platform is growing very fast. This makes the demand for an effective short video recommendation system very urgent. An effective short video recommendation system can improve user experience and user stickiness, thereby bringing huge commercial value to the platform. [0003] In recent years, many researchers have proposed video-based personalized recommendation methods. These methods can be classified into three categories: collaborative filtering...

AI Technical Summary

Problems solved by technology

[0004] The above methods all take into account the sequentiality of the user's short video click sequence, but they do not take into account the sequentiality of different aspect spaces in the short video sequence

For example, in terms of video themes, after watching cat-themed short videos, users may prefer to watch dog-themed short videos; in terms of video emotion, after watching passionate short videos, users may prefer to watch calm emotional ones short video

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