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Advertisement click rate estimation method based on decision tree, application recommendation method and device

An advertisement click and decision tree technology, applied in the computer field, can solve problems such as the inability to accurately know users, the inability to realize the application software recommendation mechanism, and the low utilization rate of recommended application software downloads.

Inactive Publication Date: 2016-06-01
BEIJING QIHOO TECH CO LTD +1
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Problems solved by technology

At the same time, due to the rapid development of the application software market and its variety, the existing technology usually randomly recommends application software to users, but cannot accurately know the needs of users, resulting in a low download utilization rate of recommended application software, and it is impossible to realize effective application software. Recommendation mechanism, based on this, it is necessary to provide a more accurate and effective application software recommendation method to meet the dual needs of merchants and users

Method used

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  • Advertisement click rate estimation method based on decision tree, application recommendation method and device
  • Advertisement click rate estimation method based on decision tree, application recommendation method and device
  • Advertisement click rate estimation method based on decision tree, application recommendation method and device

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specific Embodiment 3

[0176] Correspondingly, in order to modularly describe the method for estimating the click-through rate of an advertisement based on a decision tree in the embodiment of the present invention, refer to Figure 4 As shown, a specific embodiment 3 is provided. A decision tree-based advertising click-through rate estimation device includes a first feature information acquisition module 11, a second feature information acquisition module 12, a cross feature vector generation module 13, and an estimation module 14. ,among them,

[0177] The first characteristic information acquiring module 11 is configured to acquire relevant characteristic information of a specific historical advertisement within a predetermined historical time period;

[0178] The historically placed advertisements specifically refer to advertisements that have been placed within the predetermined historical time period, which are displayed on the user interface in various forms, such as the search list of the search e...

specific Embodiment 4

[0203] At the same time, in order to make a modular description of the application software recommendation method according to the embodiment of the present invention, refer to Figure 5 As shown, a specific embodiment 4 is provided. An application software recommendation device includes a first feature information acquisition module 21, a second feature information acquisition module 22, a cross feature vector generation module 23, an estimation module 24, and a ranking module 25, wherein ,

[0204] The first characteristic information acquiring module 21 is configured to acquire relevant characteristic information of multiple historically placed application software within a predetermined historical time period;

[0205] The historical release application software specifically refers to the application software that has been released within the predetermined historical time period, which is displayed to the user in various forms, such as through the application mall, the message ...

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Abstract

The present invention provides an advertisement click rate estimation method based on a decision tree and a device. The method comprises a step of obtaining the related characteristic information of a specific historical advertisement in a predetermined historical time period, a step of obtaining the personalized characteristic information of a target user, a step of carrying out decision tree model calculation based on the obtained target user personalized characteristic information and the related characteristic information of the specific historical advertisement so as to determine a cross characteristic vector for estimating the click rate of the specific historical advertisement, wherein each leaf node of the decision tree model represents one cross characteristic, a step of calculating based on the cross characteristic vector and the model training parameter obtained from training in advance so as to estimate the click rate of the specific historical advertisement. The invention also provides an application software recommendation method and a device. According to the method or the device, through the decision tree model, the cross characteristic with a strong classification characteristic is generated, and thus the advertisement click rate is accurately and rapidly estimated.

Description

Technical field [0001] The present invention relates to the field of computer technology. Specifically, the present invention relates to a method and device for estimating advertisement click rate based on a decision tree, and further relates to a method and device for recommending application software. Background technique [0002] With the rapid development of the Internet, the popularization of network applications, and the gradual enrichment of business types in different fields, online advertising has become a new profit method for major websites, search engines, and various terminal device applications. With the continuous growth of the Internet advertising market, its effectiveness is becoming more and more important. Among them, the value used to characterize the click-through rate of advertisements, namely CRT, is a key link in the calculation of advertisements on the Internet, and its estimation accuracy directly affects the company's advertising revenue. CRT in the us...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
CPCG06Q30/0245G06Q30/0255G06Q30/0271G06Q30/0277
Inventor 周楠常富洋岳华东
Owner BEIJING QIHOO TECH CO LTD
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