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1.发明申请Targeting Ads by Effectively Combining Behavioral Targeting and Social Networking 审中-公开通过有效结合行为定位和社交网络来定位广告公开(公告)号：US20100076850A1
公开(公告)日：2010-03-25
申请号：US12235414
申请日：2008-09-22
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q30/0269
摘要： A method and system are provided for targeting ads by effectively combining behavioral targeting and social networking. In one example, the method includes receiving a behavioral targeting model to predict a propensity of each consumer in a network to select (e.g., click) an ad of a particular category based on a behavior of each consumer, training a social network model to predict a propensity of a particular consumer to select an ad of the particular category based on features derived from a social network of the particular consumer, and training an ensemble classifier to decide when to trust the behavioral targeting model and when to defer to the social model for predicting a propensity of the particular consumer to select an ad of the particular category.
摘要翻译： 提供了一种通过有效地结合行为定位和社交网络来定位广告的方法和系统。 在一个示例中，该方法包括接收行为定位模型以基于每个消费者的行为来预测网络中每个消费者的倾向来选择（例如，点击）特定类别的广告，训练社交网络模型以预测 特定消费者倾向于基于从特定消费者的社交网络导出的特征来选择特定类别的广告，以及训练集合分类器来决定何时信任行为定位模型以及何时推迟到社会模型 预测特定消费者选择特定类别的广告的倾向。
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公开(公告)号：US20120221387A1
公开(公告)日：2012-08-30
申请号：US13034342
申请日：2011-02-24
申请人： Kun Liu , Abraham Bagherjeiran , Vijay K. Narayanan , Rajen Subba , Lei Tang
发明人： Kun Liu , Abraham Bagherjeiran , Vijay K. Narayanan , Rajen Subba , Lei Tang
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0269 , G06Q50/01
摘要： A system for incentivizing sharing advertisements (“ads”) and associated deals with others includes a processor programmed to transmit to a user, for display in an application window of a communication device of a user, an advertisement and an associated deal with an economic incentive for sharing the advertisement with first persons in a social network of the user. The system tracks and stores referral activity by the first persons in the social network of the user in relation to the advertisement, the referral activity including the first persons sharing the advertisement with second persons. The system tracks and stores conversion activity such as purchasing by the first persons in the social network of the user in relation to the deal and purchasing by second persons referred by the first persons. The system delivers the economic incentive to the user for sharing with the first persons; for the first and second persons who share the advertisement; and/or for the first and second persons who convert the deal.
摘要翻译： 用于激励共享广告（“广告”）和与他人相关联的交易的系统包括被编程为传送给用户的处理器，用于在用户的通信设备的应用窗口中显示广告和与经济激励相关联的交易 用于在用户的社交网络中与第一人共享广告。 该系统跟踪和存储用户社交网络中与广告有关的第一人员的转介活动，转介活动包括与第二人共享广告的第一人。 系统跟踪和存储转换活动，例如第一人员在用户的社会网络中购买有关交易并由第一人员提及的第二人购买。 系统向用户提供经济激励，与第一人员共享; 分享广告的第一和第二人; 和/或转换交易的第一和第二人。
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公开(公告)号：US09087332B2
公开(公告)日：2015-07-21
申请号：US12871775
申请日：2010-08-30
申请人： Abraham Bagherjeiran , Renjie Tang , Zengvan Zhang , Andrew Hatch , Adwait Ratnaparkhi , Ralesh Parekh
发明人： Abraham Bagherjeiran , Renjie Tang , Zengvan Zhang , Andrew Hatch , Adwait Ratnaparkhi , Ralesh Parekh
CPC分类号： G06Q30/00 , G06Q30/0251 , G06Q30/0255 , G06Q30/0269
摘要： A method for adaptive display of internet advertisements to look-alike users using a desired user profile dataset as a seed to machine learning modules. Upon availability of a desired user profile, that user profile is mapped other look-alike users (from a larger database of users). The method proceeds to normalize the desired user profile object, proceeds to normalize known user profile objects, then seeding a machine-learning training model with the normalized desired user profile object. A scoring engine uses the normalized user profiles for matching based on extracted features (i.e. extracted from the normalized user profile objects). Once look-alike users have been identified, the internet display system may serve advertisements to the look-alike users, and analyze look-alike users' behaviors for storing the predicted similar user profile objects into the desired user profile object dataset, thus adapting to changing user behavior.
摘要翻译： 一种用于使用期望的用户简档数据集作为种子加工学习模块来将互联网广告自适应地显示为类似用户的方法。 在可获得期望的用户简档之后，该用户简档被映射到其他外观用户（来自较大的用户数据库）。 该方法继续使所需的用户简档对象规范化，继续归一化已知的用户简档对象，然后使用归一化的所需用户简档对象播放机器学习训练模型。 评分引擎使用归一化的用户简档来基于提取的特征进行匹配（即从归一化的用户简档对象中提取）。 一旦看起来相似的用户已经被识别，互联网显示系统可以向类似用户提供广告，并且分析类似用户的行为以将预测的类似用户简档对象存储到期望的用户简档对象数据集中，从而适应于 改变用户行为。
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公开(公告)号：US20120054040A1
公开(公告)日：2012-03-01
申请号：US12871775
申请日：2010-08-30
申请人： Abraham Bagherjeiran , Renjie Tang , Zengvan Zhang , Andrew Hatch , Adwait Ratnaparkhi , Ralesh Parekh
发明人： Abraham Bagherjeiran , Renjie Tang , Zengvan Zhang , Andrew Hatch , Adwait Ratnaparkhi , Ralesh Parekh
CPC分类号： G06Q30/00 , G06Q30/0251 , G06Q30/0255 , G06Q30/0269
摘要： A method for adaptive display of internet advertisements to look-alike users using a desired user profile dataset as a seed to machine learning modules. Upon availability of a desired user profile, that user profile is mapped other look-alike users (from a larger database of users). The method proceeds to normalize the desired user profile object, proceeds to normalize known user profile objects, then seeding a machine-learning training model with the normalized desired user profile object. A scoring engine uses the normalized user profiles for matching based on extracted features (i.e. extracted from the normalized user profile objects). Once look-alike users have been identified, the internet display system may serve advertisements to the look-alike users, and analyze look-alike users' behaviors for storing the predicted similar user profile objects into the desired user profile object dataset, thus adapting to changing user behavior.
摘要翻译： 一种用于使用期望的用户简档数据集作为种子加工学习模块来将互联网广告自适应地显示为类似用户的方法。 在可获得期望的用户简档之后，该用户简档被映射到其他外观用户（来自较大的用户数据库）。 该方法继续使所需的用户简档对象规范化，继续归一化已知的用户简档对象，然后使用归一化的所需用户简档对象播放机器学习训练模型。 评分引擎使用归一化的用户简档来基于提取的特征进行匹配（即从归一化的用户简档对象中提取）。 一旦看起来相似的用户已经被识别，互联网显示系统可以向类似用户提供广告，并且分析类似用户的行为以将预测的类似用户简档对象存储到期望的用户简档对象数据集中，从而适应 改变用户行为。
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5.发明申请SYSTEM AND METHOD FOR FINDING CONNECTED COMPONENTS IN A LARGE-SCALE GRAPH 审中-公开用于在大规模图中发现连接组件的系统和方法公开(公告)号：US20100083194A1
公开(公告)日：2010-04-01
申请号：US12239770
申请日：2008-09-27
IPC分类号： G06F17/50
CPC分类号： G06F17/509 , G06F17/10
摘要： An improved system and method for finding connected components in a large-scale graph is provided. In a map-reduce framework, subsets of a collection of edges for unique vertices may be distributed to several mappers. Connected components of subgraphs represented by each subset of edges may be computed by each mapper. Then the sets of edges for connected components of subgraphs may be sorted by vertex. The sets of edges representing connected components of subgraphs may be distributed to one or more reducers to find maximal sets of weakly connected components of the large-scale graph. The sorted sets of edges for each vertex representing the maximal sets of connected components for subgraphs may be merged by a reducer to identify maximal sets of connected components of a graph, and the maximal sets of connected components of a graph may be output.
摘要翻译： 提供了一种用于在大型图中查找连接的组件的改进的系统和方法。 在map-reduce框架中，用于唯一顶点的边集合的子集可以分布到多个映射器。 由每个边缘子集表示的子图的连接分量可以由每个映射器计算。 然后可以通过顶点对子图的连接组件的边缘集合进行排序。 表示子图的连接分量的边缘集合可以被分配给一个或多个减速器以找到大尺度图的最弱的弱连接分量集合。 表示用于子图的连接组件的最大组的每个顶点的排序集合可以由减法器合并以识别图的连通分量的最大集合，并且可以输出图的连通分量的最大集合。
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公开(公告)号：US08533043B2
公开(公告)日：2013-09-10
申请号：US12751937
申请日：2010-03-31
IPC分类号： G06Q30/00
CPC分类号： G06Q30/00 , G06Q30/0243
摘要： An online advertising selects online advertisements for display on a network location taking into account a probability that a candidate online advertisement will receive a click on a particular website. The system may determine a network location identity of the network location and transform a set of advertisements into a set of ranked advertisements. The system may determine an advertisement rank of a first advertisement among the set of ranked advertisements. The system then may generate a click probability value. The click probability value may reflect a click probability of the first advertisement by dividing an exponent of a weighted sum of the network location identity and the advertisement rank by one plus the exponent of the weighted sum of the network location identity and the advertisement rank.
摘要翻译： 在线广告选择网络广告以在网络位置上显示，同时考虑到候选网络广告将在特定网站上接收到点击的概率。 系统可以确定网络位置的网络位置标识，并将一组广告变换成一组排名广告。 所述系统可以确定所述一组排名广告中的第一广告的广告等级。 然后，系统可以生成点击概率值。 点击概率值可以通过将网络位置身份和广告等级的加权和的指数除以加上网络位置身份和广告等级的加权和的指数之一来反映第一广告的点击概率。
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公开(公告)号：US20110246285A1
公开(公告)日：2011-10-06
申请号：US12751937
申请日：2010-03-31
CPC分类号： G06Q30/00 , G06Q30/0243
摘要： An online advertising selects online advertisements for display on a network location taking into account a probability that a candidate online advertisement will receive a click on a particular website. The system may determine a network location identity of the network location and transform a set of advertisements into a set of ranked advertisements. The system may determine an advertisement rank of a first advertisement among the set of ranked advertisements. The system then may generate a click probability value. The click probability value may reflect a click probability of the first advertisement by dividing an exponent of a weighted sum of the network location identity and the advertisement rank by one plus the exponent of the weighted sum of the network location identity and the advertisement rank.
摘要翻译： 在线广告选择网络广告以在网络位置上显示，同时考虑到候选网络广告将在特定网站上接收到点击的概率。 系统可以确定网络位置的网络位置标识，并将一组广告变换成一组排名广告。 所述系统可以确定所述一组排名广告中的第一广告的广告等级。 然后，系统可以生成点击概率值。 点击概率值可以通过将网络位置身份和广告等级的加权和的指数除以加上网络位置身份和广告等级的加权和的指数之一来反映第一广告的点击概率。
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公开(公告)号：US20100082400A1
公开(公告)日：2010-04-01
申请号：US12240675
申请日：2008-09-29
CPC分类号： G06Q30/02 , G06N5/003 , G06N20/20 , G06Q30/0248 , G06Q30/04
摘要： Machine learning techniques are employed to build and evolve classifiers (e.g., decision trees or other rule-based classifiers) which generate scores representing confidence values associated with particular paths through a classifier (rather than discrete class labels), and then compare those scores to tunable thresholds to effect classification.
摘要翻译： 使用机器学习技术来构建和演化分类器（例如，决策树或其他基于规则的分类器），其产生表示通过分类器（而不是离散类标签）与特定路径相关联的置信度值的分数，然后将这些分数与可调参数 阈值进行分类。
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