Advertisement recommendation method and apparatus

An advertisement and recommendation request technology, applied in the computer field, can solve the problems of deep mining without considering the real emotional orientation and interest level of advertisements, low accuracy of advertisement recommendation, and difficulty in identifying the negative emotions of users. Precise, precision-enhancing effects

Active Publication Date: 2015-10-07
SHENZHEN TENCENT COMP SYST CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Existing advertising recommendation methods are often based on the user's basic information and the method of modeling the click behavior of the individual to realize the advertisement recommendation, but this recommendation method does not consider the deep mining of the user's true emotional orientation and interest in the ad

Method used

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  • Advertisement recommendation method and apparatus
  • Advertisement recommendation method and apparatus
  • Advertisement recommendation method and apparatus

Examples

Experimental program
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Example Embodiment

[0029] figure 1 An application environment diagram for the operation of the advertisement recommendation method in an embodiment, such as figure 1 As shown, the application environment includes a terminal 110 and a server 120, and the terminal 110 and the server 120 communicate through a network.

[0030] The terminal 110 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The server 120 may respond to the request sent by the terminal 110 .

[0031] In one embodiment, figure 1 The internal structure of the server 120 in figure 2 As shown, the server 120 includes a processor, a storage medium, a memory, and a network interface connected through a system bus. Wherein, the storage medium of the server 120 stores an operating system, a database, and a device for advertising recommendation. The database is used to store data, such as storing advertising resources, user information, and user tags. 120 methods of a...

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Abstract

The present invention relates to an advertisement recommendation method, comprising: receiving an advertisement recommendation request; acquiring a user tag that is modified according to feedback information and that corresponds to a user in the advertisement recommendation request, wherein the feedback information comprises positive feedback information and negative feedback information; screening out a candidate recommendation advertisement according to the modified user tag; calculating, according to an estimated click model, an estimated click possibility of each candidate recommendation advertisement for the user on a current display page; calculating, according to an estimated non-interest model, an estimated non-interest possibility of each candidate recommendation advertisement for the user on the current display page; and screening out a target recommendation advertisement from the candidate recommendation advertisement according to the estimated click possibility and the estimated non-interest possibility. In an advertisement recommendation process, both positive feedback information and negative feedback information are considered, and advertisement recommendation accuracy is improved. Further, an advertisement recommendation apparatus is further provided.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to an advertisement recommendation method and device. Background technique [0002] With the development of Internet technology, obtaining information through the Internet, life, entertainment and work have become a part of people's lives. In order to increase their popularity and promote their products, merchants often place advertisements through the Internet. [0003] Existing advertising recommendation methods are often based on the user's basic information and the method of modeling the click behavior of the individual to realize the advertisement recommendation, but this recommendation method does not consider the deep mining of the user's true emotional orientation and interest in the advertisement, especially It is difficult to identify the user's negative emotions, and even the behavior of the user's negative emotions may be identified as the behavior that the us...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q30/02
CPCG06F16/9535G06Q30/0251
Inventor 吕培立肖磊刘大鹏罗川江
Owner SHENZHEN TENCENT COMP SYST CO LTD
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