Content push method and device, and computer device

A content and algorithm technology, applied in the Internet field, can solve the problems that the model Z is not the optimal model, the user cannot push the content, the estimated value of the click rate is inaccurate, etc., to achieve the effect of improving the accuracy rate

Active Publication Date: 2018-01-19
ALIBABA (CHINA) CO LTD
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AI Technical Summary

Problems solved by technology

And if there is only one LR model, it is to cater to both the A user group and the B user group to generate a compromise model Z, but model Z is not the optimal model
Therefore, the traditional LR model is not accurate in predicting the user's click rate on the application, which leads to the inability to accurately push relevant content to the user according to the traditional LR model

Method used

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  • Content push method and device, and computer device
  • Content push method and device, and computer device
  • Content push method and device, and computer device

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specific Embodiment approach

[0077] All the exposure events of the latest day are used to generate the input variables of the training samples according to the above method. For each sample, each sample will belong to a group in each iteration. For example: the number of clusters is 3, sample x is a negative sample, the value calculated by the LR model of "group 1" is 0.3 in a certain iteration process, the value calculated by the LR model of "group 2" is 0.2, and the value calculated by the LR model of "group 2" is 0.2. The value calculated by the LR model of 3" is 0.1, then the error of the LR model of "group 3" is the smallest, and the category of sample x is "group 3". Use the decision tree algorithm to establish a grouping model.

[0078] C(X)=Card(X).

[0079] Among them, Card(X) is a decision tree algorithm, and the training of this method uses the industry-wide model Card classification algorithm, which will not be repeated here.

[0080] In one embodiment, as image 3 As shown, the methods for...

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Abstract

The invention provides a content push method and device, and a computer device. The content push method includes the steps that user characteristics of an exposure application program and click behavior of a corresponding application program are acquired; a sample is generated according to the user characteristics and the click behavior of the application program; the sample is inputted into a grouping model to determine a group to which the sample belongs in a grouping model; the sample is inputted into a corresponding click rate prediction model of the group, and the estimated click rate ofthe application program by a user is obtained; the content is pushed to the user according to the estimated click rate of the application program. After the group to which the user belongs is determined by the content push method, the estimated click rate is obtained according to the click rate prediction model of the group, the accuracy of the estimated click rate is improved, and the content ispushed to the user accurately.

Description

technical field [0001] The present invention relates to the technical field of the Internet, in particular, the present invention relates to a content pushing method, device and computer equipment. Background technique [0002] LR (Logistic Regression, logistic regression) model is one of the discrete choice method models, and the logistic regression model is the earliest discrete choice model, and it is also the most widely used model at present. The logistic regression model is also a classification model in machine learning. Due to the simplicity and efficiency of the algorithm, it is widely used in practice. [0003] At present, in app stores, the most widely used model for predicting the click-through rate of applications (APPs) is the LR model. The idea is to intersect user characteristics and application characteristics as the input characteristics of the final model, and train the LR model. A big disadvantage of this approach is that it is a model for all users. In ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08H04L12/24
CPCH04L65/40
Inventor 潘岸腾
Owner ALIBABA (CHINA) CO LTD
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