专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

US20100010895A1 PREDICTION OF A DEGREE OF RELEVANCE BETWEEN QUERY REWRITES AND A SEARCH QUERY 审中-公开
标题翻译：预测QUERY REWRITES与搜索查询之间的相关程度
公开(公告)号：US20100010895A1
公开(公告)日：2010-01-14
申请号：US12169218
申请日：2008-07-08
申请人： Evgeniy Gabrilovich , Donald Metzler , Vanja Josifovski , Andrei Broder , Vassilis Plachouras , Vanessa Murdock , Massimiliano Ciaramita
发明人： Evgeniy Gabrilovich , Donald Metzler , Vanja Josifovski , Andrei Broder , Vassilis Plachouras , Vanessa Murdock , Massimiliano Ciaramita
IPC分类号： G06Q30/00 , G06F17/30 , G06F7/06
CPC分类号： G06Q30/0256 , G06Q30/02
摘要： A predictor for determining a degree of relevance between a query rewrite and a search query is provided. The predictor may receive a search query from a user via a terminal and identify a set of candidate query rewrites associated with the search query. The predictor may then extract a set of features from advertisements associated with the query rewrites and the search query and determine a degree of relevance between the advertisements and the search query based on a prediction model. The predictor may then determine the degree of relevance between the rewrites and the search query based on the determined degree of relevance between the advertisements and the search query.
摘要翻译：提供了用于确定查询重写和搜索查询之间的相关程度的预测器。预测器可以经由终端从用户接收搜索查询，并且识别与搜索查询相关联的一组候选查询重写。然后，预测器可以从与查询重写和搜索查询相关联的广告提取一组特征，并且基于预测模型确定广告和搜索查询之间的相关程度。然后，预测器可以基于所确定的广告与搜索查询之间的相关度来确定重写和搜索查询之间的相关程度。

2. 发明申请

US20090265290A1 OPTIMIZING RANKING FUNCTIONS USING CLICK DATA 审中-公开
标题翻译：使用点击数据优化排名函数
公开(公告)号：US20090265290A1
公开(公告)日：2009-10-22
申请号：US12106226
申请日：2008-04-18
申请人： Massimiliano Ciaramita , Vassilis Plachouras , Vanessa Murdock
发明人： Massimiliano Ciaramita , Vassilis Plachouras , Vanessa Murdock
IPC分类号： G06F15/18
CPC分类号： G06N3/08 , G06Q30/02
摘要： A system for optimizing machine-learned ranking functions based on click data. The system determines the weighting for each feature of a plurality of features according to a learning model based on the click data. The system selects an element from a plurality of elements for display on a web page based on the weighting of each feature of the plurality of features. The system may rank the items to form a list on the web page based on the weighted features in order of inferred relevance according to the online learning model.
摘要翻译：一种基于点击数据优化机器学习排名功能的系统。根据基于点击数据的学习模型，系统确定多个特征的每个特征的权重。该系统基于多个特征中的每个特征的加权从多个元素中选择要在网页上显示的元素。系统可以根据在线学习模型，根据推断的相关性的顺序，基于加权特征在网页上对这些项进行排序以形成列表。

3. 发明申请

US20090024554A1 Method For Matching Electronic Advertisements To Surrounding Context Based On Their Advertisement Content 有权
标题翻译：基于广告内容的电子广告与周边环境匹配的方法
公开(公告)号：US20090024554A1
公开(公告)日：2009-01-22
申请号：US11778540
申请日：2007-07-16
申请人： Vanessa Murdock , Vassilis Plachouras , Massimiliano Ciaramita
发明人： Vanessa Murdock , Vassilis Plachouras , Massimiliano Ciaramita
IPC分类号： G06N5/02
CPC分类号： G06Q30/02 , G06Q30/0276
摘要： A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.
摘要翻译：公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。为了选择广告，系统采取内容匹配的方法，重点是捕获周围内容和广告内容之间的细微的语言关联。本发明的系统通过简单而有效的语义关联度量来实现这一目标，这些措施涉及诸如“大哥”或“强茶”等常规多字表达的词汇搭配。语义关联度量被用作训练机器学习模型的特征。在一个实施例中，经训练以识别与特定上下文相关的广告的排名SVM（支持向量机）。训练后的机器学习模型然后可以用于通过向机器学习模型提供广告和周围环境的语义关联度量来对特定上下文的广告进行排名。

4. 发明申请

US20120109758A1 Method For Matching Electronic Advertisements To Surrounding Context Based On Their Advertisement Content 审中-公开
标题翻译：基于广告内容的电子广告与周边环境匹配的方法
公开(公告)号：US20120109758A1
公开(公告)日：2012-05-03
申请号：US13280111
申请日：2011-10-24
申请人： Vanessa Murdock , Vassilis Plachouras , Massimiliano Ciaramita
发明人： Vanessa Murdock , Vassilis Plachouras , Massimiliano Ciaramita
IPC分类号： G06Q30/02
CPC分类号： G06Q30/02 , G06Q30/0276
摘要： A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.
摘要翻译：公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。为了选择广告，系统采取内容匹配的方法，重点是捕获周围内容和广告内容之间的细微的语言关联。本发明的系统通过简单而有效的语义关联度量来实现这一目标，这些措施涉及诸如“大哥”或“强茶”等常规多字表达的词汇搭配。语义关联度量被用作训练机器学习模型的特征。在一个实施例中，经训练以识别与特定上下文相关的广告的排名SVM（支持向量机）。训练后的机器学习模型然后可以用于通过向机器学习模型提供广告和周围环境的语义关联度量来对特定上下文的广告进行排名。

5. 发明授权

US07912843B2 Method for selecting electronic advertisements using machine translation techniques 有权
标题翻译：使用机器翻译技术选择电子广告的方法
公开(公告)号：US07912843B2
公开(公告)日：2011-03-22
申请号：US11926568
申请日：2007-10-29
申请人： Vanessa Murdock , Massimiliano Ciaramita , Vassilis Plachouras
发明人： Vanessa Murdock , Massimiliano Ciaramita , Vassilis Plachouras
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06Q30/02
摘要： A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.
摘要翻译：公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。为了选择广告，系统采取利用机器翻译技术的内容匹配的方法。本发明的系统通过简单有效的机器翻译功能来实现这一目标，这些机器翻译功能是从周围环境中提取的，以与潜在的广告池相匹配。机器翻译功能用作训练机器学习模型的特征。在一个实施例中，经训练以识别与特定上下文相关的广告的排名SVM（支持向量机）。训练后的机器学习模型然后可以用于通过向机器学习模型提供用于广告和周围环境的机器翻译特征措施来对特定上下文的广告进行排名。

6. 发明申请

US20090282016A1 Systems and Methods for Building a Prediction Model to Predict a Degree of Relevance Between Digital Ads and a Search Query or Webpage Content 审中-公开
标题翻译：用于构建预测模型以预测数字广告与搜索查询或网页内容之间的相关程度的系统和方法
公开(公告)号：US20090282016A1
公开(公告)日：2009-11-12
申请号：US12116747
申请日：2008-05-07
申请人： Evgeniy Gabrilovich , Vassilis Plachouras , Andrei Broder , Vanessa Murdock , Donald Metzler , Vanja Josifovski , Massimiliano Ciaramita , Marcus Fontoura
发明人： Evgeniy Gabrilovich , Vassilis Plachouras , Andrei Broder , Vanessa Murdock , Donald Metzler , Vanja Josifovski , Massimiliano Ciaramita , Marcus Fontoura
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , G06F16/951 , G06Q10/04
摘要： Systems and methods for building a prediction model to predict a degree of relevance between digital ads and a search query or webpage content are disclosed. Generally, an indication of relevance is received between a plurality of digital ads and one of a webpage content or a search query. A set of features is extracted from the plurality of digital ads and one of the webpage content or the search query. A prediction model is then built to predict a degree of relevance between the set of candidate digital ads and one of a second webpage content or a second search query, where the prediction model is built based at least one the received indication of relevance and the extracted set of features.
摘要翻译：公开了用于构建预测模型以预测数字广告与搜索查询或网页内容之间的相关程度的系统和方法。通常，在多个数字广告和网页内容之一或搜索查询之间接收到相关性的指示。从多个数字广告和网页内容或搜索查询之一提取一组特征。然后构建预测模型以预测候选数字广告集合与第二网页内容或第二搜索查询之一之间的相关程度，其中基于所接收到的相关性指示中的至少一个构建预测模型，并且提取一套功能。

7. 发明申请

US20090282014A1 Systems and Methods for Predicting a Degree of Relevance Between Digital Ads and a Search Query 审中-公开
标题翻译：用于预测数字广告与搜索查询之间相关程度的系统和方法
公开(公告)号：US20090282014A1
公开(公告)日：2009-11-12
申请号：US12116710
申请日：2008-05-07
申请人： Evgeniy Gabrilovich , Vassilis Plachouras , Andrei Broder , Vanessa Murdock , Donald Metzler , Vanja Josifovski , Massimiliano Ciaramita , Marcus Fontoura
发明人： Evgeniy Gabrilovich , Vassilis Plachouras , Andrei Broder , Vanessa Murdock , Donald Metzler , Vanja Josifovski , Massimiliano Ciaramita , Marcus Fontoura
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , G06F16/334 , G06F16/951
摘要： Systems and methods for predicting a degree of relevance between a set of candidate digital ads and a search query are disclosed. Generally, an ad provider receives a digital ad request associated with a search query. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the search query associated with the digital ad request, and determines a degree of relevance between the set of candidate digital ads and the search query based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the search query, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request.
摘要翻译：公开了用于预测一组候选数字广告与搜索查询之间的相关程度的系统和方法。通常，广告提供商接收与搜索查询相关联的数字广告请求。广告提供商确定可以针对数字广告请求提供的一组候选数字广告。相关性模块从所述候选数字广告集合和与数字广告请求相关联的搜索查询提取一组特征，并且基于预测模型确定候选数字广告集合和搜索查询之间的相关程度，并且提取一组功能。如果相关性模块确定候选数字广告的集合与搜索查询相关，则广告提供者可以响应于接收到的数字广告请求，从一组候选数字广告服务一个或多个数字广告。

8. 发明申请

US20090265230A1 RANKING USING WORD OVERLAP AND CORRELATION FEATURES 有权
标题翻译：排名使用词汇覆盖和相关特征
公开(公告)号：US20090265230A1
公开(公告)日：2009-10-22
申请号：US12106230
申请日：2008-04-18
申请人： Vassilis Plachouras , Vanessa Murdock , Massimiliano Ciaramita
发明人： Vassilis Plachouras , Vanessa Murdock , Massimiliano Ciaramita
IPC分类号： G06Q30/00 , G06F17/30
CPC分类号： G06Q30/02 , G06Q30/0256
摘要： A system for and method for ranking results. The system includes a server configured to receive a query and an advertisement engine configured to receive the query from the server. The advertisement engine ranks advertisements based on various features, including at least one word overlap feature and a correlation feature.
摘要翻译：用于评估结果的系统和方法。该系统包括被配置为接收查询的服务器和被配置为从服务器接收查询的广告引擎。广告引擎基于各种特征来排列广告，包括至少一个单词重叠特征和相关特征。

9. 发明申请

US20090112840A1 Method For Selecting Electronic Advertisements Using Machine Translation Techniques 有权
标题翻译：使用机器翻译技术选择电子广告的方法
公开(公告)号：US20090112840A1
公开(公告)日：2009-04-30
申请号：US11926568
申请日：2007-10-29
申请人： Vanessa Murdock , Massimiliano Ciaramita , Vassilis Plachouras
发明人： Vanessa Murdock , Massimiliano Ciaramita , Vassilis Plachouras
IPC分类号： G06F17/30
CPC分类号： G06Q30/02
摘要： A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.
摘要翻译：公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。为了选择广告，系统采取利用机器翻译技术的内容匹配的方法。本发明的系统通过简单有效的机器翻译功能来实现这一目标，这些机器翻译功能是从周围环境中提取的，以与潜在的广告池相匹配。机器翻译功能用作训练机器学习模型的特征。在一个实施例中，经训练以识别与特定上下文相关的广告的排名SVM（支持向量机）。训练后的机器学习模型然后可以用于通过向机器学习模型提供用于广告和周围环境的机器翻译特征措施来对特定上下文的广告进行排名。

10. 发明授权

US08918328B2 Ranking using word overlap and correlation features 有权
标题翻译：使用单词重叠和相关特征排列
公开(公告)号：US08918328B2
公开(公告)日：2014-12-23
申请号：US12106230
申请日：2008-04-18
申请人： Vassilis Plachouras , Vanessa Murdock , Massimiliano Ciaramita
发明人： Vassilis Plachouras , Vanessa Murdock , Massimiliano Ciaramita
IPC分类号： G06Q30/02
CPC分类号： G06Q30/02 , G06Q30/0256
摘要： A system for and method for ranking results. The system includes a server configured to receive a query and an advertisement engine configured to receive the query from the server. The advertisement engine ranks advertisements based on various features, including at least one word overlap feature and a correlation feature.
摘要翻译：用于评估结果的系统和方法。该系统包括被配置为接收查询的服务器和被配置为从服务器接收查询的广告引擎。广告引擎基于各种特征来排列广告，包括至少一个单词重叠特征和相关特征。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式