公开(公告)号：US08484077B2

公开(公告)日：2013-07-09

申请号：US12893939

申请日：2010-09-29

申请人： Ozgur Cetin ,  Eren Manavoglu ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

发明人： Ozgur Cetin ,  Eren Manavoglu ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

IPC分类号： G06Q30/00

CPC分类号： G06Q30/0277 ,  G06Q10/04 ,  G06Q30/0241

摘要： A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.

摘要翻译： 一种将在线广告系统中的点击模型的多种概率组合成组合预测模型的方法，该方法通过接收特征集切片（例如，对应于人口统计学或分类或群集）开始，并且使用分片数据来训练多个切片 - 明智的预测模型。 训练的切片预测模型通过在训练的切片预测模型上重叠加权分布模型来组合。 然后，组合预测模型用于预测在线广告中给予查询广告对的点击的概率。 该方法可以灵活地接收切片规格，并且可以覆盖各种分布模型中的任何一个或多个，例如线性组合或对数线性组合。 使用适当的加权分布模型，组合预测模型可靠地产生至少与切片预测模型集中的最佳预测模型一样好的点击事件发生的预测估计。

公开(公告)号：US08229786B2

公开(公告)日：2012-07-24

申请号：US12755226

申请日：2010-04-06

申请人： Ozgur Cetin ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

发明人： Ozgur Cetin ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

IPC分类号： G06Q30/00

CPC分类号： G06Q30/00 ,  G06Q30/0207 ,  G06Q30/0243

摘要： Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

摘要翻译： 赞助搜索广告利用点击概率作为选择和排列与搜索结果一起显示的广告的一个因素。 点击的概率也可以被称为可以乘以广告商针对特定广告的出价来对广告的显示进行排名的预测点击率（“CTR”）。 对点击概率的准确预测提高了每次点击付费系统中广告产生的潜在收入。 其他广告系统可能会从广告在不同环境和场景中的点击概率的准确可靠估计中获益。

公开(公告)号：US20110246286A1

公开(公告)日：2011-10-06

申请号：US12755226

申请日：2010-04-06

申请人： Ozgur Cetin ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

发明人： Ozgur Cetin ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

IPC分类号： G06Q30/00 ,  G06F17/30

CPC分类号： G06Q30/00 ,  G06Q30/0207 ,  G06Q30/0243

摘要： Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

摘要翻译： 赞助搜索广告利用点击概率作为选择和排列与搜索结果一起显示的广告的一个因素。 点击的概率也可以被称为可以乘以广告商针对特定广告的出价来对广告的显示进行排名的预测点击率（“CTR”）。 对点击概率的准确预测提高了每次点击付费系统中广告产生的潜在收入。 其他广告系统可能会从广告在不同环境和场景中的点击概率的准确可靠估计中获益。

公开(公告)号：US20120022952A1

公开(公告)日：2012-01-26

申请号：US12893939

申请日：2010-09-29

申请人： Ozgur Cetin ,  Eren Manavoglu ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

发明人： Ozgur Cetin ,  Eren Manavoglu ,  Kannan Achan ,  Erick Cantu-Paz ,  Rukmini Iyer

IPC分类号： G06Q30/00

CPC分类号： G06Q30/0277 ,  G06Q10/04 ,  G06Q30/0241

摘要： A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.

摘要翻译： 一种将在线广告系统中的点击模型的多种概率组合成组合预测模型的方法，该方法通过接收特征集切片（例如，对应于人口统计学或分类或群集）开始，并且使用分片数据来训练多个切片 - 明智的预测模型。 训练的切片预测模型通过在训练的切片预测模型上重叠加权分布模型来组合。 然后，组合预测模型用于预测在线广告中给予查询广告对的点击的概率。 该方法可以灵活地接收切片规格，并且可以覆盖各种分布模型中的任何一个或多个，例如线性组合或对数线性组合。 使用适当的加权分布模型，组合预测模型可靠地产生至少与切片预测模型集中的最佳预测模型一样好的点击事件发生的预测估计。

公开(公告)号：US20120136722A1

公开(公告)日：2012-05-31

申请号：US12956496

申请日：2010-11-30

申请人： Divy Kothiwal ,  Kannan Achan ,  Eren Manavoglu ,  Erick Cantu-Paz

发明人： Divy Kothiwal ,  Kannan Achan ,  Eren Manavoglu ,  Erick Cantu-Paz

IPC分类号： G06Q30/00

CPC分类号： G06Q30/0256 ,  G06Q30/0241 ,  G06Q30/0251

摘要： A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically). A system for implementing the method includes aggregating data from a historical database using selectors such as a position selector, a click feature selector, an impression-advertiser-campaign-creative selector, and a commercial intent selector.

摘要翻译： 一种计算机实现的方法和系统，用于使用用于从历史数据库确定意图驱动的点击的技术，基于用户的商业意图（涉及主题广告）在赞助搜索系统中选择主题广告。 该方法包括用于聚合训练模型数据集的步骤，其中训练模型数据集包含所选择的点击历史。 然后，从训练模型数据集中选择点击的图表（进一步选择点击次数），点击的点击包括一组点击的广告，以及计算主题广告的意图驱动的点击反馈值。 该方法包括使用与在特定时间段（静态或动态确定的时间段）内接收的点击对应的特征来选择点击的平板的技术。 用于实现该方法的系统包括使用诸如位置选择器，点击特征选择器，展示广告商 - 广告系列创意选择器和商业意图选择器之类的选择器来汇总来自历史数据库的数据。

公开(公告)号：US08364525B2

公开(公告)日：2013-01-29

申请号：US12956496

申请日：2010-11-30

申请人： Divy Kothiwal ,  Kannan Achan ,  Eren Manavoglu ,  Erick Cantu-Paz

发明人： Divy Kothiwal ,  Kannan Achan ,  Eren Manavoglu ,  Erick Cantu-Paz

IPC分类号： G06Q30/00

CPC分类号： G06Q30/0256 ,  G06Q30/0241 ,  G06Q30/0251

摘要： A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically). A system for implementing the method includes aggregating data from a historical database using selectors such as a position selector, a click feature selector, an impression-advertiser-campaign-creative selector, and a commercial intent selector.

摘要翻译： 一种计算机实现的方法和系统，用于使用用于从历史数据库确定意图驱动的点击的技术，基于用户的商业意图（涉及主题广告）在赞助搜索系统中选择主题广告。 该方法包括用于聚合训练模型数据集的步骤，其中训练模型数据集包含所选择的点击历史。 然后，从训练模型数据集中选择点击的图表（进一步选择点击次数），点击的图片包括一组点击的广告，以及计算该主题广告的意图驱动的点击反馈值。 该方法包括使用与在特定时间段（静态或动态确定的时间段）内接收的点击对应的特征来选择点击的平板的技术。 用于实现该方法的系统包括使用诸如位置选择器，点击特征选择器，展示广告商 - 广告系列创意选择器和商业意图选择器之类的选择器来汇总来自历史数据库的数据。

公开(公告)号：US08745731B2

公开(公告)日：2014-06-03

申请号：US12061664

申请日：2008-04-03

申请人： Kannan Achan ,  Yinglian Xie ,  Fang Yu

发明人： Kannan Achan ,  Yinglian Xie ,  Fang Yu

IPC分类号： G06F11/00 ,  G06F12/14 ,  G06F12/16 ,  G06F7/04

CPC分类号： H04L63/1441 ,  H04L2463/144

摘要： Identification and prevention of email spam that originates from botnets may be performed by finding similarity in their host property and behavior patterns using a set of labeled data. Clustering models of host properties pertaining to previously identified and appropriately tagged botnet hosts may be learned. Given labeled data, each botnet may be examined individually and a clustering model learned to reflect upon a set of selected host properties. Once a model has been learned for every botnet, clustering behavior may be used to look for host properties that fit into a profile. Such traffic can be either discarded or tagged for subsequent analysis and can also be used to profile botnets preventing them from launching other attacks. In addition, models of individual botnets can be further clustered to form superclusters, which can help understand botnet behavior and detect future attacks.

摘要翻译： 识别和预防来自僵尸网络的电子邮件垃圾邮件可以通过使用一组标签数据来查找其主机属性和行为模式的相似性来执行。 可以了解与以前识别和适当标记的僵尸网络主机相关的主机属性的聚类模型。 给定标签数据，可以单独检查每个僵尸网络，并且学习聚类模型以反映一组选定的主机属性。 一旦为每个僵尸网络学习了一个模型，可以使用聚类行为来查找适合于配置文件的主机属性。 这样的流量可以被丢弃或被标记用于后续分析，并且还可以用于描述僵尸网络，防止他们发起其他攻击。 另外，个人僵尸网络的模型可以进一步集群以形成超级集群，这可以帮助了解僵尸网络行为并检测未来的攻击。

公开(公告)号：US08010341B2

公开(公告)日：2011-08-30

申请号：US11855099

申请日：2007-09-13

申请人： Kannan Achan ,  Moises Goldszmidt ,  Lev Ratinov

发明人： Kannan Achan ,  Moises Goldszmidt ,  Lev Ratinov

IPC分类号： G06F17/27 ,  G06F17/20 ,  G06F15/18 ,  G10L15/06 ,  G10L15/14

CPC分类号： G10L15/142 ,  G06F17/2715 ,  G06K9/6297

摘要： Mechanisms are disclosed for incorporating prototype information into probabilistic models for automated information processing, mining, and knowledge discovery. Examples of these models include Hidden Markov Models (HMMs), Latent Dirichlet Allocation (LDA) models, and the like. The prototype information injects prior knowledge to such models, thereby rendering them more accurate, effective, and efficient. For instance, in the context of automated word labeling, additional knowledge is encoded into the models by providing a small set of prototypical words for each possible label. The net result is that words in a given corpus are labeled and are therefore in condition to be summarized, identified, classified, clustered, and the like.

摘要翻译： 公开了将原型信息并入用于自动化信息处理，挖掘和知识发现的概率模型中的机制。 这些模型的示例包括隐马尔可夫模型（HMM），潜在狄利克雷分配（LDA）模型等。 原型信息将先前的知识注入到这些模型中，从而使它们更准确，有效和高效。 例如，在自动化字标识的上下文中，通过为每个可能的标签提供一小组原型字来将附加知识编码到模型中。 最终的结果是，给定语料库中的单词被标记，因此在其中被概括，识别，分类，聚类等等。

公开(公告)号：US08856360B2

公开(公告)日：2014-10-07

申请号：US11821211

申请日：2007-06-22

申请人： Kannan Achan ,  Eliot Gillum ,  Yinglian Xie ,  Fang Yu

发明人： Kannan Achan ,  Eliot Gillum ,  Yinglian Xie ,  Fang Yu

IPC分类号： G06F15/16 ,  H04L29/06 ,  H04L29/12

CPC分类号： H04L63/1408 ,  H04L29/12783 ,  H04L61/35

摘要： Dynamic IP addresses may be automatically identified and their dynamics patterns may be analyzed. Multi-user IP address blocks are determined as candidates for further analysis. An entropy score is determined for each IP address in every candidate block to distinguish between a dynamic IP and a static IP shared by multiple users. IP addresses with high entropy scores are grouped, and then analyzed, and may be used in various applications, such as spam filtering.

摘要翻译： 可以自动识别动态IP地址，并且可以分析其动态模式。 多用户IP地址块被确定为进一步分析的候选者。 为每个候选块中的每个IP地址确定熵分数，以区分动态IP和由多个用户共享的静态IP。 具有高熵分数的IP地址被分组，然后分析，并且可以用于各种应用中，例如垃圾邮件过滤。

公开(公告)号：US20090254989A1

公开(公告)日：2009-10-08

申请号：US12061664

申请日：2008-04-03

申请人： Kannan Achan ,  Yinglian Xie ,  Fang Yu

发明人： Kannan Achan ,  Yinglian Xie ,  Fang Yu

IPC分类号： G06F11/00 ,  G06F9/455

CPC分类号： H04L63/1441 ,  H04L2463/144

摘要： Identification and prevention of email spam that originates from botnets may be performed by finding similarity in their host property and behavior patterns using a set of labeled data. Clustering models of host properties pertaining to previously identified and appropriately tagged botnet hosts may be learned. Given labeled data, each botnet may be examined individually and a clustering model learned to reflect upon a set of selected host properties. Once a model has been learned for every botnet, clustering behavior may be used to look for host properties that fit into a profile. Such traffic can be either discarded or tagged for subsequent analysis and can also be used to profile botnets preventing them from launching other attacks. In addition, models of individual botnets can be further clustered to form superclusters, which can help understand botnet behavior and detect future attacks.

摘要翻译： 识别和预防来自僵尸网络的电子邮件垃圾邮件可以通过使用一组标签数据来查找其主机属性和行为模式的相似性来执行。 可以了解与以前识别和适当标记的僵尸网络主机相关的主机属性的聚类模型。 给定标签数据，可以单独检查每个僵尸网络，并且学习聚类模型以反映一组选定的主机属性。 一旦为每个僵尸网络学习了一个模型，可以使用聚类行为来查找适合于配置文件的主机属性。 这样的流量可以被丢弃或被标记用于后续分析，并且还可以用于描述僵尸网络，防止他们发起其他攻击。 另外，个人僵尸网络的模型可以进一步集群以形成超级集群，这可以帮助了解僵尸网络行为并检测未来的攻击。