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    • 2. 发明申请
    • DETERMINING QUERY INTENT
    • 确定查询内容
    • US20110314012A1
    • 2011-12-22
    • US12816389
    • 2010-06-16
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • G06F17/30
    • G06F17/30979
    • A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.
    • 树结构具有与项目类别的层次结构的每个类别相关联的节点。 树的子节点与与父节点相关联的类别的子类别相关联。 通过将每个查询与对应于所选择的查询类别的节点相关联,将包括接收到的查询和针对每个接收到的查询的所选项目类别的指示符的训练数据与树结构组合。 当接收到查询时,分类器被应用于节点以产生查询旨在匹配与节点相关联的类别的项目的概率。 应用分类器直到概率低于阈值。 选择与接收到的查询的意图最接近的节点相关联的一个或多个类别,并输出与接收到的查询匹配的那些类别的项目的指示符。
    • 3. 发明授权
    • Determining query intent
    • 确定查询意图
    • US08612432B2
    • 2013-12-17
    • US12816389
    • 2010-06-16
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • G06F7/00G06F17/30G06F15/18
    • G06F17/30979
    • A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.
    • 树结构具有与项目类别的层次结构的每个类别相关联的节点。 树的子节点与与父节点相关联的类别的子类别相关联。 通过将每个查询与对应于所选择的查询类别的节点相关联,将包括接收到的查询和针对每个接收到的查询的所选项目类别的指示符的训练数据与树结构组合。 当接收到查询时,分类器被应用于节点以产生查询旨在匹配与节点相关联的类别的项目的概率。 应用分类器直到概率低于阈值。 选择与接收到的查询的意图最接近的节点相关联的一个或多个类别,并输出与接收到的查询匹配的那些类别的项目的指示符。
    • 4. 发明申请
    • RATING COMPUTATION ON SOCIAL NETWORKS
    • 社会网络评估计算
    • US20090306996A1
    • 2009-12-10
    • US12133370
    • 2008-06-05
    • Panayiotis TsaparasKrishnaram N. G. KenthapadiAlan Halverson
    • Panayiotis TsaparasKrishnaram N. G. KenthapadiAlan Halverson
    • G06Q99/00
    • G06Q30/02G06Q50/01
    • A social network may be used to determine a rating of a user with no prior history. The ratings for unrated nodes may be inferred from the existing ratings of users associated with the unrated node in either or both the underlying social network or other social networks. Additionally in some implementations, the effect of the rating of a rated node to an unrated node diminishes as the strength of their relationships decreases. In some cases, a social network may be modeled as an electrical network, and ratings may be modeled as voltages on the nodes of the social network, relationships in the social network may be modeled as connections in the electrical network, and in some cases the strength of relationships may be modeled as conductance of the connections. Ratings for nodes may be determined using Kirchhoff's Law and in some cases by solving a set of linear equations or by propagating positive and negative ratings using a random walk with absorbing states.
    • 可以使用社交网络来确定没有先前历史的用户的评级。 对未分配节点的评级可以从在基础社交网络或其他社交网络中的任一个或两者中与未分级节点相关联的用户的现有评级推断。 另外在一些实施方式中,额定节点的额定值对未分级节点的影响随着其关系的强度减小而减小。 在某些情况下,社交网络可能被建模为电网,评级可以被建模为社交网络节点上的电压,社交网络中的关系可以被建模为电网中的连接,并且在某些情况下 关系的力量可能被建模为连接的传导。 可以使用基尔霍夫定律确定节点的等级,并且在某些情况下通过求解一组线性方程式,或者通过使用具有吸收状态的随机游走来传播正和负的额定值。
    • 8. 发明申请
    • OBJECT CLASSIFICATION USING TAXONOMIES
    • 使用TAXONOMIES的对象分类
    • US20100185577A1
    • 2010-07-22
    • US12414065
    • 2009-03-30
    • Panayiotis TsaparasPanagiotis PapadimitriouAriel D. FuxmanLise C. GetoorRakesh Agrawal
    • Panayiotis TsaparasPanagiotis PapadimitriouAriel D. FuxmanLise C. GetoorRakesh Agrawal
    • G06N5/02
    • G06N99/005
    • As provided herein objects from a source catalog, such as a provider's catalog, can be added to a target catalog, such as an enterprise master catalog, in a scalable manner utilizing catalog taxonomies. A baseline classifier determines probabilities for source objects to target catalog classes. Source objects can be assigned to those classes with probabilities that meet a desired threshold and meet a desired rate. A classification cost for target classes can be determined for respective unassigned source objects, which can comprise determining an assignment cost and separation cost for the source objects for respective desired target classes. The separation and assignment costs can be combined to determine the classification cost, and the unassigned source objects can be assigned to those classes having a desired classification cost.
    • 如本文所提供的,可以使用目录分类法将来自源目录的诸如提供者目录的对象以可扩展的方式添加到目标目录,例如企业主目录。 基准分类器确定源对象到目标目录类的概率。 可以将源对象分配给具有满足期望阈值且满足期望速率的概率的那些类。 可以针对相应的未分配的源对象来确定目标类别的分类成本,其可以包括确定用于各个期望目标类别的源对象的分配成本和分离成本。 分离和分配成本可以组合以确定分类成本,并且未分配的源对象可以被分配给具有期望的分类成本的那些类。
    • 9. 发明授权
    • Object classification using taxonomies
    • 使用分类法的对象分类
    • US08275726B2
    • 2012-09-25
    • US12414065
    • 2009-03-30
    • Panayiotis TsaparasPanagiotis PapadimitriouAriel D. FuxmanLise C. GetoorRakesh Agrawal
    • Panayiotis TsaparasPanagiotis PapadimitriouAriel D. FuxmanLise C. GetoorRakesh Agrawal
    • G06E1/00G06E3/00G06F15/18G06G7/00
    • G06N99/005
    • As provided herein objects from a source catalog, such as a provider's catalog, can be added to a target catalog, such as an enterprise master catalog, in a scalable manner utilizing catalog taxonomies. A baseline classifier determines probabilities for source objects to target catalog classes. Source objects can be assigned to those classes with probabilities that meet a desired threshold and meet a desired rate. A classification cost for target classes can be determined for respective unassigned source objects, which can comprise determining an assignment cost and separation cost for the source objects for respective desired target classes. The separation and assignment costs can be combined to determine the classification cost, and the unassigned source objects can be assigned to those classes having a desired classification cost.
    • 如本文所提供的,可以使用目录分类法将来自源目录的诸如提供者目录的对象以可扩展的方式添加到目标目录,例如企业主目录。 基准分类器确定源对象到目标目录类的概率。 可以将源对象分配给具有满足期望阈值且满足期望速率的概率的那些类。 可以针对相应的未分配的源对象来确定目标类别的分类成本,其可以包括确定用于各个期望目标类别的源对象的分配成本和分离成本。 分离和分配成本可以组合以确定分类成本,并且未分配的源对象可以被分配给具有期望的分类成本的那些类。