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1. 发明授权

US08661042B2 Collaborative filtering with hashing 有权
标题翻译：用哈希进行协同过滤
公开(公告)号：US08661042B2
公开(公告)日：2014-02-25
申请号：US12906551
申请日：2010-10-18
申请人： Martin B. Scholz , Shyamsundar Rajaram , Rajan Lukose
发明人： Martin B. Scholz , Shyamsundar Rajaram , Rajan Lukose
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30699
摘要： Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in a binary ratings matrix having a particular dimensional space. Unknown values in the binary ratings matrix are weighted with a weight matrix having the particular dimensional space. The binary ratings matrix and the weight matrix are hashed into a lower dimensional space by one of row and column. The hashed binary ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the binary ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.
摘要翻译：提供系统，方法和机器可读和可执行指令用于协同过滤。协同过滤包括用具有特定尺寸空间的二进制评级矩阵中的行和列来表示用户和对象。二进制等级矩阵中的未知值用具有特定尺寸空间的权重矩阵加权。二进制等级矩阵和权重矩阵通过行和列之一被散列成较低维的空间。散列二进制等级矩阵和散列权重矩阵是通过交替的最小二乘法近似的低阶。使用二进制等级矩阵和权重矩阵来更新行和列之一的低阶近似的结果。可以基于更新的结果为一个用户生成其中一个对象的推荐。

2. 发明授权

US08631017B2 Collaborative filtering with hashing 有权
标题翻译：用哈希进行协同过滤
公开(公告)号：US08631017B2
公开(公告)日：2014-01-14
申请号：US12970262
申请日：2010-12-16
申请人： Martin B. Scholz , Shyamsundar Rajaram , Rajan Lukose
发明人： Martin B. Scholz , Shyamsundar Rajaram , Rajan Lukose
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/3053
摘要： Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in an ordinal ratings matrix having a particular dimensional space. Values in the ordinal ratings matrix are weighted with a weight matrix having the particular dimensional space. The weight matrix is hashed into a lower dimensional space by one of row and column by multiplying a projection matrix by the weight matrix. The ordinal ratings matrix is hashed into a lower dimensional space by multiplying the projection matrix by an element-wise product of the weight matrix and the ordinal ratings matrix to form a reduced ratings matrix, and element-wise dividing the reduced ratings matrix by the hashed weight matrix. The hashed ordinal ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the ordinal ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.
摘要翻译：提供系统，方法和机器可读和可执行指令用于协同过滤。协作过滤包括用具有特定尺寸空间的序数等级矩阵中的行和列表示用户和对象。使用具有特定尺寸空间的权重矩阵对序数等级矩阵中的值进行加权。通过将投影矩阵乘以权重矩阵，权重矩阵通过行和列之一被散列成较低维空间。通过将投影矩阵乘以权重矩阵和序数等级矩阵的元素乘积来将序数等级矩阵散列到较低维空间中，以形成减小的等级矩阵，并且将分数矩阵除以散列重量矩阵。散列序数等级矩阵和散列权重矩阵由交替的最小二乘法近似近似。使用序数等级矩阵和权重矩阵来更新行和列之一的低阶近似的结果。可以基于更新的结果为一个用户生成其中一个对象的推荐。

3. 发明申请

US20120158741A1 COLLABORATIVE FILTERING WITH HASHING 有权
标题翻译：与洗涤的协同过滤
公开(公告)号：US20120158741A1
公开(公告)日：2012-06-21
申请号：US12970262
申请日：2010-12-16
申请人： Martin B. Scholz , Shyamsundar Rajaram , Rajan Lukose
发明人： Martin B. Scholz , Shyamsundar Rajaram , Rajan Lukose
IPC分类号： G06F17/30
CPC分类号： G06F17/3053
摘要： Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in an ordinal ratings matrix having a particular dimensional space. Values in the ordinal ratings matrix are weighted with a weight matrix having the particular dimensional space. The weight matrix is hashed into a lower dimensional space by one of row and column by multiplying a projection matrix by the weight matrix. The ordinal ratings matrix is hashed into a lower dimensional space by multiplying the projection matrix by an element-wise product of the weight matrix and the ordinal ratings matrix to form a reduced ratings matrix, and element-wise dividing the reduced ratings matrix by the hashed weight matrix. The hashed ordinal ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the ordinal ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.
摘要翻译：提供系统，方法和机器可读和可执行指令用于协同过滤。协作过滤包括用具有特定尺寸空间的序数等级矩阵中的行和列表示用户和对象。使用具有特定尺寸空间的权重矩阵对序数等级矩阵中的值进行加权。通过将投影矩阵乘以权重矩阵，权重矩阵通过行和列之一被散列成较低维空间。通过将投影矩阵乘以权重矩阵和序数等级矩阵的元素乘积来将序数等级矩阵散列到较低维空间中，以形成减小的等级矩阵，并且将分数矩阵除以散列重量矩阵。散列序数等级矩阵和散列权重矩阵是通过交替的最小二乘法近似的低阶。使用序数等级矩阵和权重矩阵来更新行和列之一的低阶近似的结果。可以基于更新的结果为一个用户生成其中一个对象的推荐。

4. 发明申请

US20120096009A1 COLLABORATIVE FILTERING WITH HASHING 有权
标题翻译：与洗涤的协同过滤
公开(公告)号：US20120096009A1
公开(公告)日：2012-04-19
申请号：US12906551
申请日：2010-10-18
申请人： Martin B. Scholz , Shyamsundar Rajaram , Rajant Lukose
发明人： Martin B. Scholz , Shyamsundar Rajaram , Rajant Lukose
IPC分类号： G06F17/30
CPC分类号： G06F17/30699
摘要： Systems, methods, and machine readable and executable instructions are provided for collaborative filtering. Collaborative filtering includes representing users and objects by rows and columns in a binary ratings matrix having a particular dimensional space. Unknown values in the binary ratings matrix are weighted with a weight matrix having the particular dimensional space. The binary ratings matrix and the weight matrix are hashed into a lower dimensional space by one of row and column. The hashed binary ratings matrix and the hashed weight matrix are low-rank approximated by alternating least squares. A result of the low-rank approximation for the one of row and column is updated using the binary ratings matrix and the weight matrix. A recommendation of one of the objects can be generated for one of the users based on the updated result.
摘要翻译：提供系统，方法和机器可读和可执行指令用于协同过滤。协同过滤包括用具有特定尺寸空间的二进制评级矩阵中的行和列来表示用户和对象。二进制等级矩阵中的未知值用具有特定尺寸空间的权重矩阵加权。二进制等级矩阵和权重矩阵通过行和列之一被散列成较低维的空间。散列二进制等级矩阵和散列权重矩阵是通过交替的最小二乘法近似的低阶。使用二进制等级矩阵和权重矩阵来更新行和列之一的低阶近似的结果。可以基于更新的结果为一个用户生成其中一个对象的推荐。

5. 发明申请

US20120005217A1 Selecting Microblog Entries Based on Web Pages, Via Path Similarity Within Hierarchy of Categories 有权
标题翻译：基于网页选择微博条目，通过类别层次结构中的路径相似性
公开(公告)号：US20120005217A1
公开(公告)日：2012-01-05
申请号：US12828240
申请日：2010-06-30
申请人： Ignacio Zendejas , Rajan Lukose , Craig Peter Sayers , Shyam Sundar Rajaram , Martin B. Scholz
发明人： Ignacio Zendejas , Rajan Lukose , Craig Peter Sayers , Shyam Sundar Rajaram , Martin B. Scholz
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： For each web page visited, a path is determined through a hierarchy of categories. The hierarchy of categories has levels from a most abstract level to a most concrete level. For each microblog entry of a microblog, a path is determined through the hierarchy of categories. Each microblog entry for which the path is similar to the path for at least one web page is determined as a selected microblog entry.
摘要翻译：对于访问的每个网页，通过类别层次来确定路径。类别的层次结构从最抽象层次到最具体的层次。对于微博的每个微博条目，通过类别的层次来确定路径。路径类似于至少一个网页的路径的每个微博条目被确定为所选微博条目。

6. 发明授权

US08086555B2 Collaborative filtering systems and methods 有权
标题翻译：协同过滤系统和方法
公开(公告)号：US08086555B2
公开(公告)日：2011-12-27
申请号：US12359167
申请日：2009-01-23
申请人： Rong Pan , Rajan Lukose , Martin B. Scholz
发明人： Rong Pan , Rajan Lukose , Martin B. Scholz
IPC分类号： G06F9/44 , G06N7/02 , G06N7/06
CPC分类号： G06F17/30029 , G06F17/30867 , G06Q10/04
摘要： A collaborative filtering method for evaluating a group of items to aid in predicting utility of items for a particular user comprises assigning an item value of either known or missing to each item of the group of items, and applying a modification scheme to the item values of the missing items to assign a confidence value to each of the item values of the missing items to thereby generate a group of modified item values. The group of items having modified item values and the group known items are evaluated to generate a prediction of utility of items for a particular user.
摘要翻译：用于评估一组项目以帮助预测特定用户的项目的效用的协同过滤方法包括将已知或缺失的项目值分配给该组项目的每个项目，并将修改方案应用于项目值丢失的项目来为缺失项目的每个项目值分配置信度值，从而生成一组修改后的项目值。对具有修改项目值的项目组和组已知项目进行评估以产生用于特定用户的项目的效用的预测。

7. 发明申请

US20100293062A1 ADVERTISEMENT SELECTION BASED ON KEY WORDS 有权
标题翻译：基于关键词的广告选择
公开(公告)号：US20100293062A1
公开(公告)日：2010-11-18
申请号：US12465947
申请日：2009-05-14
申请人： Rajan LUKOSE , Martin B. Scholz , Shyam S. Rajaram
发明人： Rajan LUKOSE , Martin B. Scholz , Shyam S. Rajaram
IPC分类号： G06Q30/00 , G06F17/30
CPC分类号： G06F17/30707 , G06Q30/02 , G06Q30/0207 , G06Q30/0277
摘要： A computer-implemented method comprises running, by a processor, a plurality of classifiers on a web page to obtain one or more keywords. The method further comprises selecting, by the processor, advertisements based on the one or more keywords.
摘要翻译：计算机实现的方法包括由处理器在网页上运行多个分类器以获得一个或多个关键字。该方法还包括由处理器基于一个或多个关键字选择广告。

8. 发明申请

US20130346188A1 Estimating Costs of behavioral Targeting 审中-公开
标题翻译：估计行为指标的成本
公开(公告)号：US20130346188A1
公开(公告)日：2013-12-26
申请号：US14003126
申请日：2011-03-15
申请人： Martin B. Scholz , Shyam Sundar Rajaram , Rajan Lukose
发明人： Martin B. Scholz , Shyam Sundar Rajaram , Rajan Lukose
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0244 , G06Q10/10 , G06Q30/02
摘要： Systems (490), methods (100, 200), and computer-readable and executable instructions (324, 424) are provided for estimating costs of behavioral targeting. Estimating costs of behavioral targeting can include scoring a topic with a behavioral targeting model (101, 201). Estimating costs of behavioral targeting can also include obtaining a plurality of data items including geographic location information (102, 202). Estimating costs of behavioral targeting can also include detecting (104, 204) and scoring (209) a sentiment from filtered data items regarding a topic within a region (104, 204). Estimating costs of behavioral targeting can include computing a penalty score for the topic in the region in response to the scored sentiment exceeding a threshold (213), (106, 206). Estimating costs of behavioral targeting can include adjusting the topic score in the region according to the penalty score (108, 208). Furthermore, estimating costs of behavioral targeting can include taking an action with respect to advertising based on the adjusted topic score (110, 210).
摘要翻译：系统（490），方法（100,200）和计算机可读和可执行指令（324,424）被提供用于估计行为目标的成本。估计行为定位成本可以包括使用行为定位模型评分主题（101,201）。估计行为定位的成本还可以包括获得包括地理位置信息（102,202）的多个数据项。估计行为定位的成本还可以包括从区域（104,204）内的关于主题的经过滤数据项的检测（104,204）和评分（209）得分（209）。估计行为定位的成本可以包括响应于超过阈值（213）（106,206）的评分情绪计算该区域中的主题的惩罚分数。估计行为定位的成本可以包括根据惩罚分数调整该地区的主题得分（108,208）。此外，估计行为定位的成本可以包括基于经调整的主题得分（110,210）采取关于广告的动作。

9. 发明授权

US09213767B2 Method and system for characterizing web content 有权
标题翻译：表征Web内容的方法和系统
公开(公告)号：US09213767B2
公开(公告)日：2015-12-15
申请号：US12538405
申请日：2009-08-10
申请人： Martin B. Scholz , Shyam Sundar Rajaram , Rajan Lukose
发明人： Martin B. Scholz , Shyam Sundar Rajaram , Rajan Lukose
IPC分类号： G06F17/30
CPC分类号： G06F17/30867 , G06F17/3071
摘要： An exemplary embodiment of the present invention provides a method of processing Web activity data. The method includes obtaining a database of Website organizational data. The method also includes generating a data structure from the database of Website organizational data comprising an Item identifier and a Website category corresponding to the item identifier. The method also includes generating a reduced-rank classification structure from the data structure, the reduced-rank classification structure including a category grouping corresponding to one or more of the Website categories.
摘要翻译：本发明的示例性实施例提供了一种处理Web活动数据的方法。该方法包括获取网站组织数据的数据库。该方法还包括从站点组织数据的数据库生成数据结构，该数据结构包括项目标识符和对应于项目标识符的网站类别。该方法还包括从数据结构生成降级分类结构，降级分类结构包括对应于一个或多个网站类别的类别分组。

10. 发明申请

US20110137904A1 CLICKSTREAMS AND WEBSITE CLASSIFICATION 有权
标题翻译： CLICKSTREAMS和网站分类
公开(公告)号：US20110137904A1
公开(公告)日：2011-06-09
申请号：US12629907
申请日：2009-12-03
申请人： Shyam Sundar Rajaram , Martin B. Scholz , Rajan Lukose
发明人： Shyam Sundar Rajaram , Martin B. Scholz , Rajan Lukose
IPC分类号： G06F17/30 , G06F15/16 , G06F15/18 , G06N5/02
CPC分类号： G06F17/30876 , G06F17/30705 , G06F17/3071 , G06Q30/02
摘要： One embodiment is a method that receives a seed Uniform Resource Locator (URL) that represents a category for website classification. Clickstream data generated from the seed URL and additional URLs are analyzed to determine whether the additional URLs belong to the category. The method selects one or more of the additional URLs to represent the category.
摘要翻译：一个实施例是接收表示用于网站分类的类别的种子统一资源定位符（URL）的方法。分析从种子URL生成的点击流数据和附加URL，以确定附加URL是否属于该类别。该方法选择一个或多个附加URL来表示类别。

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