EFFICIENT SIMILARITY RANKING FOR BIPARTITE GRAPHS
    1.
    发明申请
    EFFICIENT SIMILARITY RANKING FOR BIPARTITE GRAPHS 审中-公开
    有效的相似性排序

    公开(公告)号:US20150220530A1

    公开(公告)日:2015-08-06

    申请号:US14278811

    申请日:2014-05-15

    Applicant: GOOGLE INC.

    CPC classification number: G06F17/30943 G06F2216/03 G06Q30/0241

    Abstract: Systems and methods offer an efficient approach to computing similarity rankings in bipartite graphs. An example system includes at least one processor and memory storing a bipartite graph having a first set and a second set of nodes, with nodes in the first set being connected to nodes in the second set by edges. The memory also stores instructions that, when executed by the at least one processor, cause the system to assign each node in the second set to one of a plurality of categories and, for each of the plurality of categories, generate a subgraph. The subgraph comprises of a subset of nodes in the first set and edges linking the nodes in the subset, where the nodes in the subset are selected based on connection to a node in the second set that is assigned to the category. The system uses the subgraph to respond to queries.

    Abstract translation: 系统和方法提供了一种有效的方法来计算二分图中的相似性排名。 示例系统包括至少一个处理器和存储具有第一组和第二组节点的二分图的存储器,其中第一组中的节点通过边缘连接到第二组中的节点。 存储器还存储指令,当由至少一个处理器执行时,使得系统将第二组中的每个节点分配给多个类别中的一个,并且对于多个类别中的每一个分类,生成子图。 子图包括第一组中的节点的子集和链接子集中的节点的边缘,其中基于与分配给该类别的第二集合中的节点的连接来选择子集中的节点。 系统使用子图来回应查询。

    Learning semantic image similarity
    2.
    发明授权
    Learning semantic image similarity 有权
    学习语义图像相似度

    公开(公告)号:US09087271B2

    公开(公告)日:2015-07-21

    申请号:US14455350

    申请日:2014-08-08

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying similar images. In some implementations, a method is provided that includes receiving a collection of images and data associated with each image in the collection of images; generating a sparse feature representation for each image in the collection of images; and training an image similarity function using image triplets sampled from the collection of images and corresponding sparse feature representations.

    Abstract translation: 方法,系统和装置,包括编码在计算机存储介质上的用于识别相似图像的计算机程序。 在一些实施方式中,提供了一种方法,其包括接收图像集合中与图像集合中的每个图像相关联的图像和数据的集合; 为图像集合中的每个图像生成稀疏特征表示; 并使用从图像集合和相应的稀疏特征表示中采样的图像三元组训练图像相似度函数。

    Learning Semantic Image Similarity
    3.
    发明申请
    Learning Semantic Image Similarity 有权
    学习语义图像相似性

    公开(公告)号:US20150161485A1

    公开(公告)日:2015-06-11

    申请号:US14455350

    申请日:2014-08-08

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying similar images. In some implementations, a method is provided that includes receiving a collection of images and data associated with each image in the collection of images; generating a sparse feature representation for each image in the collection of images; and training an image similarity function using image triplets sampled from the collection of images and corresponding sparse feature representations.

    Abstract translation: 方法,系统和装置,包括编码在计算机存储介质上的用于识别相似图像的计算机程序。 在一些实施方式中,提供了一种方法,其包括接收图像集合中与图像集合中的每个图像相关联的图像和数据的集合; 为图像集合中的每个图像生成稀疏特征表示; 并使用从图像集合和相应的稀疏特征表示中采样的图像三元组训练图像相似度函数。

    Efficient similarity ranking for bipartite graphs

    公开(公告)号:US10152557B2

    公开(公告)日:2018-12-11

    申请号:US14278811

    申请日:2014-05-15

    Applicant: GOOGLE INC.

    Abstract: Systems and methods offer an efficient approach to computing similarity rankings in bipartite graphs. An example system includes at least one processor and memory storing a bipartite graph having a first set and a second set of nodes, with nodes in the first set being connected to nodes in the second set by edges. The memory also stores instructions that, when executed by the at least one processor, cause the system to assign each node in the second set to one of a plurality of categories and, for each of the plurality of categories, generate a subgraph. The subgraph comprises of a subset of nodes in the first set and edges linking the nodes in the subset, where the nodes in the subset are selected based on connection to a node in the second set that is assigned to the category. The system uses the subgraph to respond to queries.

    EXTERNALITIES IN AN AUCTION
    5.
    发明申请
    EXTERNALITIES IN AN AUCTION 审中-公开
    拍卖中的外部事件

    公开(公告)号:US20140214555A1

    公开(公告)日:2014-07-31

    申请号:US13753865

    申请日:2013-01-30

    Applicant: Google Inc.

    CPC classification number: G06Q30/0275 G06Q30/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for reporting an externality effect. A method includes: identifying an auction associated with a content item request, the auction including one or more eligible inventory items for presentation responsive to the content item request and including a first inventory item associated with a first entity; evaluating results of the auction including determining an externality effect of another eligible inventory item in the auction on the first inventory item; and reporting the externality effect.

    Abstract translation: 方法,系统和装置,包括编码在计算机可读存储介质上的用于报告外部效应的计算机程序。 一种方法包括:识别与内容项目请求相关联的拍卖,所述拍卖包括响应于所述内容项目请求进行呈现的一个或多个合格清单项,并且包括与第一实体相关联的第一库存项目; 评估拍卖的结果,包括确定第一个库存项目中拍卖中另一个合格的库存项目的外部效应; 并报告外部效应。

    Learning semantic image similarity
    6.
    发明授权
    Learning semantic image similarity 有权
    学习语义图像相似度

    公开(公告)号:US08805812B1

    公开(公告)日:2014-08-12

    申请号:US13867937

    申请日:2013-04-22

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying similar images. In some implementations, a method is provided that includes receiving a collection of images and data associated with each image in the collection of images; generating a sparse feature representation for each image in the collection of images; and training an image similarity function using image triplets sampled from the collection of images and corresponding sparse feature representations.

    Abstract translation: 方法,系统和装置,包括编码在计算机存储介质上的用于识别相似图像的计算机程序。 在一些实施方式中,提供了一种方法,其包括接收图像集合中与图像集合中的每个图像相关联的图像和数据的集合; 为图像集合中的每个图像生成稀疏特征表示; 并使用从图像集合和相应的稀疏特征表示中采样的图像三元组训练图像相似度函数。

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