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Webpage advertisement putting device and method based on multilayer random hidden feature model

A technology of advertising placement and hidden features, applied in the field of advertising, can solve the problems of low advertising recommendation efficiency and sparse data, and achieve the effect of efficient and accurate advertising recommendation, wide application, and accurate delivery.

Pending Publication Date: 2020-12-04
SHENZHEN WANJIAAN IOT TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the existence of a large number of users and a large number of advertisements, and when the click-through rate of advertisements is very low, such as the proportion of the click-through rate of advertisements is between 1 / 10,000 and 1 / 1,000, it will lead to the problem of data sparseness, so that the efficiency of advertisement recommendation is not good. high

Method used

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  • Webpage advertisement putting device and method based on multilayer random hidden feature model
  • Webpage advertisement putting device and method based on multilayer random hidden feature model
  • Webpage advertisement putting device and method based on multilayer random hidden feature model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] Embodiment 1: see figure 1 , a web page advertisement delivery device based on a multi-layer random latent feature model, the device includes:

[0051] The advertising data collection module 110 is used to collect and store user's advertising behavior data. The advertisement behavior data refers to the interaction record between the user and the advertisement.

[0052] The data conversion module 120 is used to convert the advertising behavior data into a target matrix R, which is a high-dimensional sparse matrix with M rows and N columns, where M represents the number of users and N represents the number of advertisements. The target matrix R refers to the matrix that uses the interaction record between the user and the advertisement to convert, and the element r in the matrix u,i Represents the element in the uth row and ith column of the matrix, if the uth user has browsed the ith advertisement, then r u,i = 1, otherwise the value is a missing value.

[0053] The ...

Embodiment 2

[0063] Example 2: see figure 2 , a web page advertisement delivery method based on a multi-layer random latent feature model, characterized in that: the method includes the following steps:

[0064] S1, collecting and storing user's advertising behavior data.

[0065] S2, converting the advertising behavior data into a target matrix and storing it for later use.

[0066] S3, based on the target matrix, randomly generate weights and offsets, use the activation function to generate a user behavior feature matrix, and then use the user behavior feature matrix to derive the advertisement feature matrix.

[0067] The weights include the first-layer weight matrix A and the multi-layer weight matrix W, which are used to add weighted items to the user behavior feature matrix in the activation function.

[0068] The bias includes the first layer bias vector b and the multi-layer bias vector d, which are used to add a bias item to the user behavior feature matrix in the activation fu...

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Abstract

The invention discloses a webpage advertisement putting device and method based on a multilayer random hidden feature model. A webpage advertisement putting device based on a multilayer random hiddenfeature model is characterized in that the device comprises an advertisement data collection module used for collecting and storing advertisement behavior data of a user; a data conversion module which is used for converting the advertisement behavior data into a target matrix; a feature updating module which is used for initializing and updating related parameters involved in the user behavior feature matrix and the advertisement feature matrix, randomly generating weights and offsets based on the target matrix, and updating the behavior characteristics of each user by using an activation function to form a user behavior characteristic matrix; combining the user behavior characteristic matrix and the target matrix to obtain an advertisement characteristic matrix; and an advertisement recommendation module which is used for obtaining advertisement weights by utilizing the user behavior feature matrix and the advertisement feature matrix, and sorting the advertisement recommendation sequence of each user according to the advertisement weights; the method can be widely applied to various internet platforms.

Description

technical field [0001] The invention relates to the field of advertising, in particular to a device and method for delivering web page advertisements based on a multi-layer random latent feature model. Background technique [0002] Advertisement is a media method used to persuade potential consumers to buy or pay attention to a certain product. From newspapers and magazines, to TV, movies, and websites, advertisements are everywhere in our lives. At the same time, with the rapid development of mobile Internet technology, the focus began to shift from traditional media advertising to mobile Internet advertising. With the good digital media environment in modern society, the advertising media industry is developing more rapidly. However, at present, many advertisements are placed randomly, and it is difficult to make dynamic adjustments according to the user's attributes and behaviors. That is, when a user comes, an advertisement is randomly selected to be served to him. To ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06F17/15G06F17/16
CPCG06Q30/0271G06Q30/0201G06Q30/0277G06F17/15G06F17/16
Inventor 张能锋袁野罗辛尚明生
Owner SHENZHEN WANJIAAN IOT TECH CO LTD
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