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Advertisement click rate estimation method

An advertising click and vector technology, applied in neural learning methods, biological neural network models, business and other directions, can solve the problem of ignoring the importance of different features in modeling

Active Publication Date: 2021-02-12
EAST CHINA UNIV OF TECH
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  • Claims
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AI Technical Summary

Problems solved by technology

[0006] Through the above analysis, the problems and defects of the existing technology are: the CTR prediction model based on deep learning ignores the importance of modeling different features

Method used

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  • Advertisement click rate estimation method
  • Advertisement click rate estimation method

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Embodiment Construction

[0069] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0070] Aiming at the problems existing in the prior art, the present invention provides a method for estimating the click-through rate of an advertisement. The present invention will be described in detail below in conjunction with the accompanying drawings.

[0071] like figure 1 As shown, the advertisement click rate estimation method provided by the embodiment of the present invention includes:

[0072] S101, feature encoding, building each component of the model one by one;

[0073] S102, establishing an embedding layer, converting the original feature into an embedding vector for the pair;

[0074] S103, establishin...

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Abstract

The invention belongs to the technical field of advertisement click rate estimation, and discloses an advertisement click rate estimation method, which comprises the steps of dividing original features into different feature domains to obtain corresponding onehot vectors; obtaining a corresponding embedded vector; carrying out compression and excitation processing on the embedded vector of each feature domain through an SENET layer, learning the importance degree of the features, carrying out re-calibration, and weighting the importance degree of the features to the original embedded vector toobtain a new embedded vector; learning second-order feature interaction information through FM, and learning high-order feature interaction information through DNN to obtain a corresponding output vector; splicing the output vectors of the FM and the DNN; and inputting a spliced result into a multi-layer perceptron, obtaining an estimated advertisement click probability through a softmax activation function, and evaluating the quality of the model. Compared with other models, the advertisement click rate prediction model is better in performance, and the accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of advertisement click rate estimation, and in particular relates to an advertisement click rate estimation method. Background technique [0002] Currently, ad click-through rate (Click-Through Rate, CTR) refers to a given user and web page content, the number of times an ad is clicked accounts for the proportion of the total number of impressions. In the current big data scenario, advertising is changing from the "extensive" delivery in the past to the "precise" delivery. Data-driven precise advertising has become the mainstream method of current advertising. Programmatic buying on the advertising demand side In the process of advertising and online delivery, it is necessary to pre-evaluate the user's preference for advertisements, and the process of measuring this important indicator is CTR estimation. CTR estimation is a key link in Internet computing advertising. The accuracy of CTR not only affects the...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06N3/04G06N3/08
CPCG06Q30/0242G06N3/08G06N3/045
Inventor 徐洪珍许杰云周菲万弘
Owner EAST CHINA UNIV OF TECH
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