Advertisement click-through rate prediction method based on multi-dimensional feature combination logical regression

Abstract

The invention discloses an advertisement click-through rate prediction method based on multi-dimensional feature combination logical regression. The method comprises the first step that feature information of a hierarchical structure of the user hierarchy, feature information of a hierarchical structure of the media hierarchy and feature information of a hierarchical structure of the advertisement hierarchy are extracted from the obtained click-through rate data respectively; the second step that multi-dimensional combination is carried out on the feature information of the hierarchical structure of the user hierarchy, the feature information of the hierarchical structure of the media hierarchy and the feature information of the hierarchical structure of the advertisement hierarchy, three-to-three combination is carried out on one-dimensional feature information in the feature information to obtain a three-dimensional feature combination, and a feature vector combined by the three-dimensional feature information is formed to represent a user cluster; the third step that the second step is carried out repeatedly and a learning set of the feature vector combined by the three-dimensional feature information is obtained; the fourth step that the learning set obtained in the third step is used for training and testing a logical regression model, and the logical regression model is used for predicting the advertisement click-through rate.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an advertisement click rate prediction method based on multidimensional feature combination logistic regression. Background technique [0002] Computational advertising is a sub-discipline that has emerged in the current demand environment. It is an advertising delivery mechanism that calculates the most matching advertisement based on a given user and web page content and performs precise targeted delivery. The Internet computing advertising industry chain includes three basic roles: advertiser (Advertiser), advertising media (Publisher), and user (User). Among them, advertisers hope to increase the possibility of users buying goods or registering on websites by placing appropriate advertisements to valid users, so as to obtain the best publicity effect; users hope to see useful advertising information instead of harassing information; publishers Therefore, in the proce...

AI Technical Summary

Problems solved by technology

[0004] The logistic regression model is often applied to the prediction of the click-through rate of advertisements because it can fit the occurrence of advertisement clicks. The prediction results are affected by many factors. However, the prediction model trained by the traditional one-dimensional feature vector model is not effective. Ad click-through rates calculated by predictive models are unreliable

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