WO2007105909A1 - Procédé et système de ciblage d'internautes en visite sur des sites publicitaires reposant sur des profils de cliquage et faisant intervenir un système de filtrage collaboratif avec réseaux neuronaux - Google Patents

Procédé et système de ciblage d'internautes en visite sur des sites publicitaires reposant sur des profils de cliquage et faisant intervenir un système de filtrage collaboratif avec réseaux neuronaux Download PDF

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Publication number
WO2007105909A1
WO2007105909A1 PCT/KR2007/001250 KR2007001250W WO2007105909A1 WO 2007105909 A1 WO2007105909 A1 WO 2007105909A1 KR 2007001250 W KR2007001250 W KR 2007001250W WO 2007105909 A1 WO2007105909 A1 WO 2007105909A1
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Prior art keywords
advertisement
relevance value
advertisements
user
information
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PCT/KR2007/001250
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English (en)
Inventor
Jinwoo Baek
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Nhn Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020060024181A external-priority patent/KR100792701B1/ko
Priority claimed from KR1020060024707A external-priority patent/KR100792700B1/ko
Application filed by Nhn Corporation filed Critical Nhn Corporation
Publication of WO2007105909A1 publication Critical patent/WO2007105909A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates to a method and system for recommending an advertisement, and more particularly, to an advertisement recommendation method and system which can collect a user's advertisement click information, calculate a relevance value between a plurality of advertisements, and recommend an advertisement having a greater relevance value with respect to the user's previously clicked advertisement.
  • a user when a user retrieves particular data or content without knowing a Universal Resource Locator (URL) or an Internet Protocol (IP) address of a corresponding site, the user may access a site of providing an Internet search service using an information search system, and enter a keyword associated with the data or the content and thereby utilize the desired data.
  • URL Universal Resource Locator
  • IP Internet Protocol
  • Sites of providing an Internet search service generally generate revenue by displaying a banner advertisement for a user who desires to utilize the Internet search service.
  • a click rate may be increased, and thus the sites may generate more revenue.
  • An aspect of the present invention provides a method and system which can recommend an advertisement, having a greater relevance value with respect to a user's previously clicked advertisement, to increase a click rate of a banner advertisement.
  • Another aspect of the present invention also provides a method and system which can recommend an advertisement, having a greater similarity value with respect to a user's previously clicked advertisement, to increase a click rate of a banner advertisement.
  • a method of recommending an advertisement including the steps of: collecting advertisement click information about each of a plurality of advertisements; calculating a relevance value between the plurality of advertisements by using the collected advertisement click information; and recommending a predetermined advertisement having a greater relevance value with respect to a user's previously clicked advertisement by using the calculated relevance value between the plurality of advertisements.
  • a method of recommending an advertisement including the steps of: collecting advertisement click information about each of a plurality of advertisements; calculating a relevance value between the plurality of advertisements by using the collected advertisement click information; training a neural network with the calculated relevance value between the plurality of advertisements; and recommending an advertisement having a greater relevance value with respect to a user's previously clicked advertisement by using the calculated relevance value between the plurality of advertisements and a result of the training.
  • a system for recommending an advertisement including: an advertisement information collector configured to collect clicked advertisement information and cookie information of a user which clicks the advertisement; a pattern extractor configured to extract an advertisement pattern by using the advertisement information and the user's cookie information; a relevance value calculator configured to calculate a relevance value between a plurality of advertisements by using the extracted advertisement pattern; a comparison component configured to compare a relevance value between the user's previously clicked advertisement and another advertisement by using the calculated relevance value between the plurality of advertisements; and a recommendation component configured to recommend an advertisement having a greater relevance value with respect to the user's previously clicked advertisement, based on a result of the comparison.
  • FIG. 1 illustrates a relation between a user terminal and an advertisement recommendation system according to an exemplary embodiment of the present invention
  • FIG. 6 illustrates an example of recommending an advertisement according to an exemplary embodiment of the present invention
  • FIG. 7 is a block diagram illustrating a configuration of an advertisement recommendation system according to an exemplary embodiment of the present invention
  • FIG. 10 is a flowchart illustrating a process of recommending an advertisement based on a result of a comparison of a similarity value or a relevance value between a plurality of advertisement according to another exemplary embodiment of the present invention.
  • the advertisement recommendation system 100 recommends a banner advertisement having a greater relevance value or a greater similarity value with respect to the user's previously clicked banner advertisement for the user of the accessed user terminal 120 via the communication network 110.
  • the advertisement recommendation system 100 may calculate a user's advertisement click information and provide an advertisement having a greater relevance value or a greater similarity value with respect to the user's previously clicked advertisement. Accordingly, it is possible to increase the user's click rate for an advertisement and thereby improve advertising effects.
  • the communication network 110 indicates a wired/wireless network which transmits various types of data between the advertisement recommendation system 100 and the user terminal 120.
  • the user terminal 120 accesses the advertisement recommendation system 100 via the communication network 110, and transmits cookie information of a user and information about whether the user clicks an advertisement provided from the advertisement recommendation system 100, to the advertisement recommendation system 100. Also, the user terminal 120 receives a recommendation advertisement from the advertisement recommendation system 100 via the communication network 110.
  • FIG. 2 is a flowchart illustrating a method of recommending an advertisement according to an exemplary embodiment of the present invention.
  • the advertisement recommendation system 100 collects advertisement click information about each of a plurality of advertisements, which are transmitted from the user terminal 120 via the communication network 110.
  • the advertisement click information includes information about whether any of the plurality of advertisement is clicked by a user, and also includes user information of the user which clicks the advertisement.
  • the advertisement recommendation system 100 may collect a user identifier included in cookie information by using the user's cookie information, and store the user identifier and a banner advertisement identifier in an advertisement database.
  • the cookie information may include a cookie identifier for the user identifier.
  • the advertisement recommendation system 100 calculates a relevance value between the plurality of advertisements by using the collected advertisement click information.
  • the advertisement recommendation system 100 extracts an advertisement pattern vector by using the collected advertisement click information, and calculates the relevance value between the plurality of advertisements by using the extracted advertisement pattern vector.
  • the advertisement recommendation system 100 recommends a predetermined advertisement having a greater relevance value with respect to a user's previously clicked advertisement by using the calculated relevance value between the plurality of advertisements.
  • the advertisement recommendation system 100 displays various types of advertisements on the accessed user terminal 120 through a webpage of the advertisement recommendation site.
  • the user terminal 120 transmits click information about the clicked advertisement to the advertisement recommendation system 100 via the communication network 110.
  • the click information indicates information about whether the user clicks the provided advertisement.
  • the advertisement recommendation system 100 determines whether a number of advertisement click vectors according to the extracted advertisement click pattern is sufficient to calculate the relevance value between the plurality of advertisements. Specifically, when an insufficient number of advertisement click vectors is extracted, a rule to calculate the relevance value between the plurality of advertisements may not be acquired. Accordingly, in operation 340, the advertisement recommendation system 100 determines whether a number of advertisement click vectors is sufficient to calculate the relevance value between the plurality of advertisements. In operation 350, the advertisement recommendation system 100 selects an appropriate advertisement click vector from the extracted advertisement click pattern. Specifically, in operation 350, the advertisement recommendation system 100 selects the appropriate advertisement click vector to calculate the relevance value between the plurality of advertisements, from the extracted advertisement click pattern.
  • the advertisement recommendation system 100 calculates the relevance value between the plurality of advertisements by using the selected advertisement click vector. Specifically, in operation 360, the advertisement recommendation system 100 calculates the relevance value between the plurality of advertisements by using a collaborative filtering algorithm with respect to the selected advertisement click vector.
  • Input data to perform the collaborative filtering algorithm according to the present invention is indicated as an m x n (user-advertisement) matrix which includes an m number of user identifiers and an n number of advertisement identifiers.
  • m x n user-advertisement matrix
  • T when the user clicks an advertisement, a corresponding value is indicated as T.
  • O' when the user does not click an advertisement, a corresponding value is indicated as 1 O'.
  • the collaborative filtering algorithm may group the advertisement identifiers, as show in FIG. 4, into, for example, " ⁇ 1,0,1,... ⁇ , ⁇ 0,1,0,... ⁇ , ⁇ 1,0,1,... ⁇ , ⁇ 0,0,0,... ⁇ , ##, and then may calculate the relevance value between the plurality of advertisements by comparing advertisement click patterns between the plurality of advertisements.
  • the advertisement recommendation system 100 may calculate a relevance value between the advertisements '? ⁇ ' and 'ty to be comparatively greater. Conversely, when the users clicking the advertisement '7p do not generally click an advertisement 'M"', the advertisement recommendation system 100 may calculate the relevance value between the advertisements '7p and ' 1 ⁇ ' to be comparatively smaller.
  • the advertisement recommendation system 100 stores the calculated relevance value between the plurality of advertisements. Specifically, in operation 370, the advertisement recommendation system 100 stores the calculated relevance value between the plurality of advertisements with respect to a plurality of advertisements click information and thereby creates a database.
  • the advertisement recommendation system 100 selects a top N number of advertisements with the greater relevance value. Specifically, in operation 380, the advertisement recommendation system 100 selects the top N number of advertisements having the greater relevance value between the plurality of advertisements, to recommend an advertisement having a greater relevance value with respect to the user's previously clicked advertisement.
  • a method of recommending an advertisement may extract advertisement click information about each of a plurality of advertisements, calculate a relevance value between the plurality of advertisements by using the extracted advertisement click information, and thereby select a top N number of advertisements having a greater relevance value between the plurality of advertisements.
  • FIG. 5 is a flowchart illustrating a process of recommending an advertisement based on a result of a comparison of a relevance value between a plurality of advertisements according to an exemplary embodiment of the present invention.
  • the advertisement recommendation site 100 receives advertisement click information about the clicked banner advertisement. Specifically, in operation 520, when the banner advertisement is provided from the webpage of the advertisement recommendation site and clicked by the user, the advertisement recommendation system 100 receives the advertisement click information including an identifier of the clicked banner advertisement.
  • the advertisement recommendation system 100 may identify information about the user, clicking the banner advertisement, and information about the clicked advertisement through operations 510 and 520.
  • the advertisement recommendation system 100 recommends the selected advertisement, and provides the recommended advertisement to the user terminal 120 accessing the webpage of the advertisement recommendation site. Specifically, in operation 550, the advertisement recommendation system 100 provides an advertisement, having a greater relevance value with respect to the user's previously clicked advertisement, as a recommendation advertisement, to the user terminal 120. Accordingly, the user of the user terminal 120 may verify the recommendation advertisement provided from the webpage of the advertisement recommendation site. When the recommendation advertisement corresponds to the user's interest field, the user may click the advertisement. Conversely, when the recommendation advertisement is out of the user's interest field, the user may not click the advertisement. In operation 560, the advertisement recommendation system 100 determines whether the user of the accessed user terminal 120 clicks the recommendation advertisement provided through the webpage of the advertisement recommendation site.
  • the advertisement recommendation system 100 terminates the advertisement recommendation process.
  • a method of recommending an advertisement according to the present invention may recommend an advertisement having a greater relevance value with respect to a user's previously clicked advertisement and thereby increase a click rate of a recommendation advertisement and also improve advertising effects.
  • FIG. 7 is a block diagram illustrating a configuration of an advertisement recommendation system 700 according to an exemplary embodiment of the present invention.
  • the advertisement recommendation system 700 includes an advertisement information collector 710, a pattern extractor 720, a relevance value calculator 730, a comparison component 740, and a recommendation component 750.
  • the advertisement information collector 710 collects advertisement click information of a clicked advertisement and user information of a user which clicks the advertisement. Specifically, to advertise a predetermined advertisement, the advertisement information collector 710 collects a plurality of advertisement click information about whether a recommendation advertisement is clicked, and cookie information of the user which clicks the advertisement.
  • the pattern extractor 720 extracts an advertisement pattern by using the advertisement click information and the user's cookie information. Specifically, the pattern extractor 720 extracts an advertisement pattern vector from a matrix as shown in
  • the matrix uses the user's cookie information and the advertisement information for a line and a column respectively.
  • the relevance value calculator 730 calculates a relevance value between a plurality of advertisements by using the extracted advertisement pattern. Specifically, the relevance value calculator 730 calculates the relevance value between the plurality of advertisements by using the extracted advertisement pattern vector. Also, the relevance value calculator 730 calculates the relevance value between the plurality of advertisements, based on a collaborative filtering algorithm, using the extracted advertising pattern vector.
  • the comparison component 740 compares a relevance value between the user's previously clicked advertisement and another advertisement by using the calculated relevance value between the plurality of advertisements. Specifically, the comparison component 740 compares a relevance value between advertisements, enrolled as a banner advertisement in response to a request from an advertiser, and the user's previously clicked advertisement by using the calculated relevance value between the plurality of advertisements. The recommendation component 750 recommends an advertisement having a greater relevance value with respect to the user's previously clicked advertisement among the enrolled advertisements, based on a result of the comparison.
  • the advertisement recommendation system 700 may collect advertisement click information of users about a banner advertisement, calculate a relevance value between a plurality of banner advertisements by using the collected advertisement click information, and recommend a banner advertisement having a greater relevance value with respect to the user's previously clicked banner advertisement. Accordingly, it is possible to improve a click rate of a recommendation advertisement and improve advertising effects.
  • FIG. 8 is a flowchart illustrating a method of recommending an advertisement according to another exemplary embodiment of the present invention.
  • the advertisement recommendation system 100 collects advertisement click information about each of a plurality of advertisements, which are transmitted from the user terminal 120 via the communication network 110.
  • the advertisement click information includes information about whether any of the plurality of advertisement is clicked by a user, and user information of the user which clicks the advertisement.
  • the advertisement recommendation system 100 may collect a user identifier included in cookie information by using the user's cookie information and store the user identifier and a banner advertisement identifier in an advertisement database.
  • the cookie information may include a cookie identifier for the user identifier.
  • the advertisement recommendation system 100 calculates a relevance value between the plurality of advertisements by using the collected advertisement click information. Specifically, in operation 820, the advertisement recommendation system 100 extracts an advertisement pattern vector by using the collected advertisement click information, and calculates the relevance value between the plurality of advertisements by using the extracted advertisement pattern vector.
  • the advertisement recommendation system 100 calculates the relevance value between the plurality of advertisements, based on a collaborative filtering algorithm, using the extracted advertisement pattern vector.
  • the collaborative filtering algorithm indicates a technique capable of identifying advertisements, which a large number of users are interested in, or advertisements with a similar pattern, based on the extracted advertisement pattern vector.
  • the collaborative filtering algorithm is used to alternatively recommend pre-clicked advertisements to users with similar interests or to recommend an advertisement associated with a user's classified interest.
  • the advertisement recommendation system 100 trains a neural network with the calculated relevance value between the plurality of advertisements. Specifically, in operation 830, the advertisement recommendation system 100 trains a self-organizing map (SOM) to find a similar advertisement by using the calculated relevance value between the plurality of advertisements. In this instance, the SOM detects similar data, based on predetermined data, through the training using an artificial intelligence neural network.
  • SOM self-organizing map
  • the advertisement recommendation system 100 recommends an advertisement having a greater relevance value with respect to a user's previously clicked advertisement by using the calculated relevance value between the plurality of advertisements and a result of the training.
  • the advertisement recommendation system 100 stores the relevance value between the plurality of advertisements and the result of the training, identifies the user and the user's previously clicked advertisement, compares a relevance value between the identified advertisement and the stored advertisement, or the result of the training, and recommends an advertisement having a greater relevance value with respect to the identified advertisement, based on a result of the comparison.
  • the advertisement recommendation system 100 recommends an advertisement having either the greater relevance value or the greater similarity value with respect to the clicked advertisement and thereby provides the recommendation advertisement to the user terminal 120 via the communication network 110.
  • the advertisement recommendation system 100 provides the second advertisement 620, having the greater relevance value with respect to the first advertisement 610, as a recommendation advertisement, to a user which clicks the first advertisement 610.
  • the first advertisement 610 and the second advertisement 620 are associated with vehicles and thus have a comparatively greater relevance value therebetween, and also has a greater similarity value therebetween. Accordingly, when the user clicks the first advertisement 610, the advertisement recommendation system 100 recommends the second advertisement 620 having the greater relevance value or the greater similarity value with respect to the first advertisement 610.
  • the second advertisement 620 is generally clicked by users which click the first advertisement 610.
  • FIG. 9 is a flowchart illustrating a process of selecting an advertisement having a greater relevance value according to another exemplary embodiment of the present invention.
  • the advertisement recommendation system 100 selects an appropriate advertisement click vector from the extracted advertisement click pattern. Specifically, in operation 950, the advertisement recommendation system 100 selects the appropriate advertisement click vector to calculate the relevance value between the plurality of advertisements, from the extracted advertisement click pattern.
  • the advertisement recommendation system 100 may train the SOM using the neural network and the calculated relevance value between the plurality of advertisements, and thereby link similar advertisements. Accordingly, in operation 970, the advertisement recommendation system 100 may classify advertisements having a greater relevance value or a greater similarity value with respect to, for example, a first advertisement, using the SOM based on the neural network.
  • the advertisement recommendation system 100 stores the calculated relevance value between the plurality of advertisements and a result of the training based on the neural network. Specifically, in operation 980, the advertisement recommendation system 100 stores the calculated relevance value between the plurality of advertisements a plurality of advertisements click information and the result of the training, and thereby makes it a database. In operation 990, the advertisement recommendation system 100 selects a top N number of advertisements with the greater relevance value. Specifically, in operation 990, the advertisement recommendation system 100 selects the top N number of advertisements having the greater relevance value between the plurality of advertisements, to recommend an advertisement having a greater relevance value with respect to the user's previously clicked advertisement.
  • FIG. 10 is a flowchart illustrating a process of recommending an advertisement based on a result of comparison of a similarity value or a relevance value between a plurality of advertisement according to another exemplary embodiment of the present invention.
  • the advertisement recommendation system 100 receives a cookie identifier of a user from the user terminal 120 via the communication network 110.
  • the user terminal 120 accesses a webpage of an advertisement recommendation site.
  • the advertisement recommendation system 100 receives cookie information from the user terminal 120, accessing the advertisement recommendation site via the communication network 110, extracts a cookie identifier from the cookie information and thereby identifies the user information.
  • the advertisement recommendation site 100 receives advertisement click information about the clicked banner advertisement. Specifically, in operation 1020, when the banner advertisement is provided from the webpage of the advertisement recommendation site and clicked by the user, the advertisement recommendation system 100 receives the advertisement click information including an identifier of the clicked banner advertisement.
  • the advertisement recommendation system 100 extracts an advertisement click vector by using the cookie identifier and the advertisement click information.
  • the advertisement recommendation system 100 compares a relevance value or a similarity value with to the clicked advertisement by using the extracted advertisement click vector. Specifically, in operation 1040, the advertisement recommendation system 100 may compare the extracted advertisement click vector and the stored relevance value or the similarity value, and thereby select an advertisement having a greater relevance value or a greater similarity value with respect to the clicked advertisement.
  • the advertisement recommendation system 100 recommends the selected advertisement, and provides the recommended advertisement to the user terminal 120 accessing the webpage of the advertisement recommendation site. Specifically, in operation 1050, the advertisement recommendation system 100 provides an advertisement, having a greater relevance value or a greater similarity value with respect to the user's previously clicked advertisement, as a recommendation advertisement, to the user terminal 120. Accordingly, the user of the user terminal 120 may verify the recommendation advertisement provided from the webpage of the advertisement recommendation site. When the recommendation advertisement corresponds to the user's interest field, the user may click the advertisement. Conversely, when the recommendation advertisement is out of the user's interest field, the user may not click the advertisement.
  • the advertisement recommendation system 100 determines whether the user of the accessed user terminal 120 clicks the recommendation advertisement provided through the webpage of the advertisement recommendation site. When the user does not click the recommendation advertisement provided through the webpage of the advertisement recommendation site, the advertisement recommendation system 100 again performs operation 1050 and recommends another advertisement, having a greater relevance value or a greater similarity value with respect to the clicked advertisement through the webpage of the advertisement recommendation site. In this instance, the advertisement recommendation system 100 may sequentially provide at least one advertisement having a greater relevance value with or a greater similarity value with respect to the clicked advertisement to the user terminal 120 until the user of the user terminal 120 clicks the recommendation advertisement provided through the webpage of the advertisement recommendation site. Also, when the user clicks the recommendation advertisement provided through the webpage of the advertisement recommendation site, the advertisement recommendation system 100 terminates the advertisement recommendation process.
  • a method of recommending an advertisement according to the present invention may recommend an advertisement having a greater relevance value or a similarity value with respect to a user's previously clicked advertisement and thereby increase a click rate of a recommendation advertisement and also improve advertising effects.
  • FIG. 11 illustrates a configuration of an advertisement recommendation system 1100 according to another exemplary embodiment of the present invention.
  • the advertisement recommendation system 1100 includes an advertisement information collector 1110, a pattern extractor 1120, a relevance value calculator 1130, a neural network 1140, a comparison component 1150, and a recommendation component 1160.
  • the advertisement information collector 1110 collects advertisement click information of an advertisement and user information of a user which clicks the advertisement. Specifically, to advertise an advertisement, the advertisement information collector 1110 collects a plurality of advertisement click information about whether a recommendation advertisement is clicked, and cookie information of the user which clicks the advertisement.
  • the relevance value calculator 1130 calculates a relevance value between a plurality of advertisements by using the extracted advertisement pattern. Specifically, the relevance value calculator 1130 calculates the relevance value between the plurality of advertisements by using the extracted advertisement pattern vector.
  • the relevance value calculator 1130 calculates the relevance value between the plurality of advertisements, based on a collaborative filtering algorithm, using the extracted advertising pattern vector.
  • the neural network 1140 is trained with the relevance value between the plurality of advertisements to find a similar advertisement. Specifically, the neural network trains an SOM to find an advertisement having a greater similarity by using the calculated relevance value between the plurality of advertisements.
  • the advertisement recommendation system 1100 may collect advertisement click information of users about a banner advertisement, calculate a relevance value between a plurality of banner advertisements by using the collected advertisement click information, and recommend a banner advertisement having a greater relevance value with respect to the user's previously clicked banner advertisement. Accordingly, it is possible to improve a click rate of a recommendation advertisement and improve advertising effects.
  • an advertisement recommendation method and system which can calculate a relevance value between a plurality of advertisements by using advertisement click information, and recommend an advertisement having a greater relevance value with respect to a user's previously clicked advertisement based on the calculated relevance value between the plurality of advertisements, and thereby can improve advertising effects.

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Abstract

Procédé et système de recommandation publicitaire permettant de recueillir des information de cliquage sur des sites publicitaires, de calculer la valeur de pertinence entre une pluralité de publicités et de recommander une publicité présentant une valeur de pertinence supérieure à celle d'une publicité cliquée antérieurement. Le procédé de recommandation de publicités englobe les opérations suivantes: collecte d'informations de cliquage sur chacune d'une pluralité de publicités; calcul d'une valeur de pertinence entre une pluralité de publicité au moyen d'informations de cliquage sur des publicités; et recommandation d'une publicité prédéterminée ayant une valeur de pertinence supérieure à celle d'une publicité cliquée antérieurement au moyen de la valeur de pertinence entre la pluralité de publicités. Spécifiquement, comme le procédé et le système de recommandation de publicités permet de calculer la valeur de pertinence entre une pluralité de publicités au moyen d'informations de cliquage et de recommander une publicité ayant une valeur de pertinence supérieure à celle d'une publicité cliquée antérieurement, il est possible d'améliorer l'impact publicitaire.
PCT/KR2007/001250 2006-03-16 2007-03-14 Procédé et système de ciblage d'internautes en visite sur des sites publicitaires reposant sur des profils de cliquage et faisant intervenir un système de filtrage collaboratif avec réseaux neuronaux WO2007105909A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR1020060024181A KR100792701B1 (ko) 2006-03-16 2006-03-16 협업 필터링 시스템을 이용하여 클릭 패턴에 기초한 웹광고 추천 방법 및 그 시스템
KR10-2006-0024181 2006-03-16
KR1020060024707A KR100792700B1 (ko) 2006-03-17 2006-03-17 신경망을 가지는 협업 필터링 시스템을 이용하여 클릭패턴에 기초한 웹 광고 추천 방법 및 그 시스템
KR10-2006-0024707 2006-03-17

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WO2014047189A3 (fr) * 2012-09-19 2015-07-16 Mumm.Com Systèmes et procédés de prise de décision d'externalisation ouverte
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CN113343110A (zh) * 2021-06-30 2021-09-03 掌阅科技股份有限公司 基于投放信息实现电子书推荐方法、电子设备及存储介质
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WO2014047189A3 (fr) * 2012-09-19 2015-07-16 Mumm.Com Systèmes et procédés de prise de décision d'externalisation ouverte
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WO2017118440A1 (fr) * 2016-01-08 2017-07-13 腾讯科技（深圳）有限公司 Procédé de traitement d'informations, serveur, terminal et support de stockage informatique
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CN108062684A (zh) * 2017-12-12 2018-05-22 北京奇艺世纪科技有限公司 一种广告的点击率预测方法及装置
CN108062684B (zh) * 2017-12-12 2021-01-22 北京奇艺世纪科技有限公司 一种广告的点击率预测方法及装置
CN109190046A (zh) * 2018-09-18 2019-01-11 北京点网聚科技有限公司 内容推荐方法、装置及内容推荐服务器
CN112418935A (zh) * 2020-11-24 2021-02-26 陈敏 基于大数据和广告推送的数据处理方法及大数据平台
CN113343110A (zh) * 2021-06-30 2021-09-03 掌阅科技股份有限公司 基于投放信息实现电子书推荐方法、电子设备及存储介质
CN118195700A (zh) * 2024-03-20 2024-06-14 北京起创科技有限公司 一种基于视觉算法的广告推荐系统方法

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