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    Collaborative filtering systems and methods
    1.
    发明授权
    Collaborative filtering systems and methods 有权
    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。

    SYSTEM AND METHOD FOR MAKING A RECOMMENDATION BASED ON USER DATA
    2.
    发明申请
    SYSTEM AND METHOD FOR MAKING A RECOMMENDATION BASED ON USER DATA 有权
    基于用户数据建立建议的系统和方法

    公开(公告)号：US20100274808A1

    公开(公告)日：2010-10-28

    申请号：US12430411

    申请日：2009-04-27

    申请人： Martin B. Scholz ,  Rong Pan ,  Rajan Lukose

    发明人： Martin B. Scholz ,  Rong Pan ,  Rajan Lukose

    IPC分类号： G06F17/30

    CPC分类号： G06Q30/0631 ,  G06F17/30345 ,  G06F17/30861 ,  G06F17/30867 ,  H04L67/025 ,  H04L67/22

    摘要： There is described a system and computer-implemented method for providing a recommendation based on a sparse pattern of data. An exemplary method comprises determining a likelihood that an item for which no user preference data is available will be preferred. The exemplary method also comprises determining a likelihood that an item for which user preference data is available for users other than a particular user will be preferred based on the likelihood that the item for which no user preference data is available will be preferred. The exemplary method additionally comprises predicting that an item for which no user preference data relative to the particular user is available will be preferred if the likelihood that the particular user will prefer the item exceeds a certain level.

    摘要翻译： 描述了一种用于基于稀疏数据模式提供推荐的系统和计算机实现的方法。 一种示例性方法包括确定优选不具有用户偏好数据可用的项目的可能性。 该示例性方法还包括基于不优选没有用户偏好数据的项目的可能性来确定用户偏好数据可用于除特定用户之外的用户可能优选的可能性。 该示例性方法还包括如果特定用户更喜欢该项目的可能性超过一定水平，则预测相对于该特定用户没有用户偏好数据可用的项目将是优选的。

    System and method for making a recommendation based on user data
    3.
    发明授权
    System and method for making a recommendation based on user data 有权

    公开(公告)号：US09633117B2

    公开(公告)日：2017-04-25

    申请号：US12430411

    申请日：2009-04-27

    申请人： Martin B. Scholz ,  Rong Pan ,  Rajan Lukose

    发明人： Martin B. Scholz ,  Rong Pan ,  Rajan Lukose

    IPC分类号： G06F17/30 ,  G06Q30/06

    CPC分类号： G06Q30/0631 ,  G06F17/30345 ,  G06F17/30861 ,  G06F17/30867 ,  H04L67/025 ,  H04L67/22

    摘要： There is described a system and computer-implemented method for providing a recommendation based on a sparse pattern of data. An exemplary method comprises determining a likelihood that an item for which no user preference data is available will be preferred. The exemplary method also comprises determining a likelihood that an item for which user preference data is available for users other than a particular user will be preferred based on the likelihood that the item for which no user preference data is available will be preferred. The exemplary method additionally comprises predicting that an item for which no user preference data relative to the particular user is available will be preferred if the likelihood that the particular user will prefer the item exceeds a certain level.

    Collaborative Filtering Systems and Methods
    4.
    发明申请
    Collaborative Filtering Systems and Methods 有权
    协同过滤系统和方法

    公开(公告)号：US20100191694A1

    公开(公告)日：2010-07-29

    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： G06N5/02 ,  G06F17/30

    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。

    RECOMMENDATION BASED ON LOW-RANK APPROXIMATION
    5.
    发明申请
    RECOMMENDATION BASED ON LOW-RANK APPROXIMATION 审中-公开
    基于低排名近似推荐

    公开(公告)号：US20100325126A1

    公开(公告)日：2010-12-23

    申请号：US12487254

    申请日：2009-06-18

    申请人： Shyam S. RAJARAM ,  Martin B. Scholz ,  Rong Pan ,  Rajan Lukose

    发明人： Shyam S. RAJARAM ,  Martin B. Scholz ,  Rong Pan ,  Rajan Lukose

    IPC分类号： G06F17/30

    CPC分类号： G06Q30/02 ,  G06F16/24578

    摘要： A system and method for providing personalized recommendations are disclosed herein. A system includes a processor and a software system executed by the processor. The software system provides a recommendation for an item. The recommendation is based on a comparison of a low-rank approximation of a domain matrix to a user profile. The user profile is based, in part, on the low-rank approximation of the domain matrix.

    摘要翻译： 本文公开了一种用于提供个性化建议的系统和方法。 系统包括由处理器执行的处理器和软件系统。 软件系统提供了一个项目的建议。 该建议基于对域矩阵的低阶近似与用户简档的比较。 用户简档部分地基于域矩阵的低等级近似。

    Collaborative filtering with hashing
    7.
    发明授权
    Collaborative filtering with hashing 有权
    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。

    Estimating Costs of behavioral Targeting
    8.
    发明申请
    Estimating Costs of behavioral Targeting 审中-公开
    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。