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1. 发明授权

US08392343B2 Estimating probabilities of events in sponsored search using adaptive models 有权
标题翻译：使用自适应模型估计赞助搜索中事件的概率
公开(公告)号：US08392343B2
公开(公告)日：2013-03-05
申请号：US12840598
申请日：2010-07-21
申请人： Ozgur Cetin , Dongwei Cao , Rukmini Iyer
发明人： Ozgur Cetin , Dongwei Cao , Rukmini Iyer
IPC分类号： G06F15/18
CPC分类号： G06Q30/02 , G06N7/005 , G06Q30/0254
摘要： A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement.
摘要翻译：一种用于通过计算和比较聚合预测模型（全局模型）和一个或多个数据切片预测模型（局部模型）来估计在线广告系统中点击事件的概率的机器学习方法。所述方法包括接收具有存储在特征集中的多个特征的训练数据，并且构建估计所处理的特征集的点击事件的概率的全局预测模型。然后，将全局预测模型划分成一个或多个数据切片训练集，用于从每个数据切片训练本地模型，然后确定特定局部模型是否估计特征集的点击事件的概率优于全球模式。给定特征集可以从历史数据收集，并且可以包括用于多个查询 - 广告对的特征向量和表示广告的点击的相应指示符。

2. 发明申请

US20120023043A1 Estimating Probabilities of Events in Sponsored Search Using Adaptive Models 有权
标题翻译：使用自适应模型估计赞助搜索中的事件的概率
公开(公告)号：US20120023043A1
公开(公告)日：2012-01-26
申请号：US12840598
申请日：2010-07-21
申请人： Ozgur Cetin , Dongwei Cao , Rukmini Iyer
发明人： Ozgur Cetin , Dongwei Cao , Rukmini Iyer
IPC分类号： G06F15/18 , G06Q30/00 , G06N5/02
CPC分类号： G06Q30/02 , G06N7/005 , G06Q30/0254
摘要： A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement.
摘要翻译：一种用于通过计算和比较聚合预测模型（全局模型）和一个或多个数据切片预测模型（局部模型）来估计在线广告系统中点击事件的概率的机器学习方法。所述方法包括接收具有存储在特征集中的多个特征的训练数据，并且构建估计所处理的特征集的点击事件的概率的全局预测模型。然后，将全局预测模型划分成一个或多个数据切片训练集，用于从每个数据切片训练本地模型，然后确定特定局部模型是否估计特征集的点击事件的概率优于全球模式。给定特征集可以从历史数据收集，并且可以包括用于多个查询 - 广告对的特征向量和表示广告的点击的相应指示符。

3. 发明授权

US08484077B2 Using linear and log-linear model combinations for estimating probabilities of events 有权
标题翻译：使用线性和对数线性模型组合来估计事件的概率
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。

4. 发明授权

US08229786B2 Click probability with missing features in sponsored search 有权
标题翻译：在赞助搜索中点击遗漏特征的概率
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。

5. 发明申请

US20110246286A1 CLICK PROBABILITY WITH MISSING FEATURES IN SPONSORED SEARCH 有权
标题翻译：点击可靠性与丢失的特征在赞助的搜索
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。

6. 发明申请

US20120022952A1 Using Linear and Log-Linear Model Combinations for Estimating Probabilities of Events 有权
标题翻译：使用线性和对数线性模型组合来估计事件的概率
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。

7. 发明申请

US20140059030A1 Translating Natural Language Utterances to Keyword Search Queries 有权
标题翻译：将自然语言语言翻译成关键字搜索查询
公开(公告)号：US20140059030A1
公开(公告)日：2014-02-27
申请号：US13592638
申请日：2012-08-23
申请人： Dilek Zeynep Hakkani-Tur , Gokhan Tur , Rukmini Iyer , Larry Paul Heck
发明人： Dilek Zeynep Hakkani-Tur , Gokhan Tur , Rukmini Iyer , Larry Paul Heck
IPC分类号： G06F17/30
CPC分类号： G06F17/3066 , G06F17/2818 , G06F17/3043 , G06F17/30654 , G06F17/30663 , G06F17/30672 , G06F17/30684 , G06F17/30864 , G06F17/30914 , G06N99/005
摘要： Natural language query translation may be provided. A statistical model may be trained to detect domains according to a plurality of query click log data. Upon receiving a natural language query, the statistical model may be used to translate the natural language query into an action. The action may then be performed and at least one result associated with performing the action may be provided.
摘要翻译：可以提供自然语言查询翻译。可以训练统计模型以根据多个查询点击日志数据来检测域。在收到自然语言查询后，统计模型可用于将自然语言查询翻译成动作。然后可以执行动作，并且可以提供与执行动作相关联的至少一个结果。

8. 发明申请

US20110131157A1 SYSTEM AND METHOD FOR PREDICTING CONTEXT-DEPENDENT TERM IMPORTANCE OF SEARCH QUERIES 审中-公开
标题翻译：用于预测搜索查询的背景相关重要性的系统和方法
公开(公告)号：US20110131157A1
公开(公告)日：2011-06-02
申请号：US12626892
申请日：2009-11-28
申请人： Rukmini Iyer , Eren Manavoglu , Hema Raghavan
发明人： Rukmini Iyer , Eren Manavoglu , Hema Raghavan
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G06Q30/0251
摘要： An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.
摘要翻译：提供了一种用于识别查询的上下文相关项重要性的改进的系统和方法。使用对查询的上下文相关项重要性的监督学习来学习查询词重要性模型，然后将其用作查询词语的重要度权重作为查询特征应用于广告预测。例如，用于查询重写的查询项重要性模型可以预测与查询匹配的重写查询与被分配为查询特征的术语重要性权重。或者用于广告预测的查询词重要性模型可以预测具有被指定为查询特征的术语重要性权重的查询的相关广告。在一个实施例中，赞助的广告选择引擎选择由查询词语重要性引擎评分的赞助广告，该查询词语重要性引擎使用术语重要性权重作为查询特征和逆文档频率权重作为广告特征来分配相关性得分。

9. 发明授权

US07631008B2 System and method for generating functions to predict the clickability of advertisements 有权
标题翻译：用于生成功能以预测广告的可点击性的系统和方法
公开(公告)号：US07631008B2
公开(公告)日：2009-12-08
申请号：US11479186
申请日：2006-06-29
申请人： Chad Carson , Ashvin Kannan , Erick Cantu-Paz , Rukmini Iyer , Pero Subasic , Christopher C. LuVogt , Christopher Leggetter , Jan Pedersen , David Cho-Lun Ku
发明人： Chad Carson , Ashvin Kannan , Erick Cantu-Paz , Rukmini Iyer , Pero Subasic , Christopher C. LuVogt , Christopher Leggetter , Jan Pedersen , David Cho-Lun Ku
IPC分类号： G06F17/30
CPC分类号： G06Q10/04 , G06Q30/02 , G06Q30/0202 , G06Q30/0246 , Y10S707/99933 , Y10S707/99943
摘要： The present invention is directed towards systems and methods for predicting a frequency with which an advertisement displayed in response to a query will be selected. The method of the present invention comprises receiving analytics data associated with a display of one or more advertisements in response to one or more queries. One or more features associated with the one or more advertisements displayed in response to the one or more queries are identified. One or more functions are generated for predicting a frequency with which a given advertisement displayed in response to a query will be selected using the analytics data and features associated with the one or more advertisements displayed in response to the one or more queries.
摘要翻译：本发明涉及用于预测响应于查询显示的广告的频率的系统和方法。本发明的方法包括响应于一个或多个查询而接收与一个或多个广告的显示相关联的分析数据。识别与响应于一个或多个查询而显示的一个或多个广告相关联的一个或多个特征。生成一个或多个功能，用于预测使用响应于一个或多个查询显示的一个或多个广告相关联的分析数据和特征来选择响应于查询而显示的给定广告的频率。

10. 发明申请

US20110131205A1 SYSTEM AND METHOD TO IDENTIFY CONTEXT-DEPENDENT TERM IMPORTANCE OF QUERIES FOR PREDICTING RELEVANT SEARCH ADVERTISEMENTS 审中-公开
标题翻译：识别相关相关重要因素的系统和方法用于预测相关搜索广告
公开(公告)号：US20110131205A1
公开(公告)日：2011-06-02
申请号：US12626894
申请日：2009-11-28
申请人： Rukmini Iyer , Eren Manavoglu , Hema Raghavan
发明人： Rukmini Iyer , Eren Manavoglu , Hema Raghavan
IPC分类号： G06F17/30
CPC分类号： G06F16/3334
摘要： An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.
摘要翻译：提供了一种用于识别查询的上下文相关项重要性的改进的系统和方法。使用对查询的上下文相关项重要性的监督学习来学习查询词重要性模型，然后将其用作查询词语的重要度权重作为查询特征应用于广告预测。例如，用于查询重写的查询项重要性模型可以预测与查询匹配的重写查询与被分配为查询特征的术语重要性权重。或者用于广告预测的查询词重要性模型可以预测具有被指定为查询特征的术语重要性权重的查询的相关广告。在一个实施例中，赞助的广告选择引擎选择由查询词语重要性引擎评分的赞助广告，该查询词语重要性引擎使用术语重要性权重作为查询特征和逆文档频率权重作为广告特征来分配相关性得分。

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