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26 条记录 (耗时 0.017 秒)

    Suggesting and/or providing targeting criteria for advertisements
    4.
    发明申请
    Suggesting and/or providing targeting criteria for advertisements 有权
    建议和/或提供广告的定位标准

    公开(公告)号：US20050228797A1

    公开(公告)日：2005-10-13

    申请号：US10750451

    申请日：2003-12-31

    申请人： Ross Koningstein ,  Valentin Spitkovsky ,  Georges Harik ,  Noam Shazeer

    发明人： Ross Koningstein ,  Valentin Spitkovsky ,  Georges Harik ,  Noam Shazeer

    IPC分类号： G06F17/00

    CPC分类号： G06Q30/0251 ,  G06F17/30619 ,  G06F17/3064 ,  G06F17/30675 ,  G06F17/3071 ,  G06F17/30752 ,  G06Q30/02 ,  G06Q30/0201

    摘要： Keyword suggestions that are category-aware (and field-proven) may be used to help advertisers better target the serving of their ads, and may reduce unused ad spot inventory. The advertiser can enter ad information, such as a creative, a landing Webpage, other keywords, etc. for example. A keyword facility may use this entered ad information as seed information to infer one or more categories. It may then request that the advertiser confirm or deny some basic feedback information (e.g., categories, Webpage information, etc.). For example, an advertiser may be provided with candidate categories and may be asked to confirm (e.g., using checkboxes) which of the categories are relevant to their ad. Keywords may be determined using at least the categories. The determined keywords may be provided to the advertiser as suggested keywords, or may automatically populate ad serving constraint information as targeting keywords. The ad server system can run a trial on the determined keywords to qualify or disqualify them as targeting keyword.

    摘要翻译： 类别感知（经实地验证）的关键字建议可用于帮助广告客户更好地定位广告投放，并可能减少未使用的广告现货库存。 广告客户可以输入广告信息，例如广告素材，着陆页面，其他关键字等。 关键字设施可以使用该输入的广告信息作为种子信息来推断一个或多个类别。 然后，它可以请求广告商确认或拒绝一些基本反馈信息（例如，类别，网页信息等）。 例如，可以向广告商提供候选类别，并且可以要求其确认（例如，使用复选框）哪些类别与他们的广告相关。 可以使用至少类别来确定关键字。 所确定的关键字可以作为建议的关键字提供给广告商，或者可以自动地将广告投放约束信息填充为定位关键字。 广告服务器系统可以对确定的关键字进行试用，以使其符合资格或取消资格作为定位关键字。

    Method and apparatus for learning a probabilistic generative model for text
    5.
    发明申请
    Method and apparatus for learning a probabilistic generative model for text 有权

    公开(公告)号：US20070208772A1

    公开(公告)日：2007-09-06

    申请号：US11796383

    申请日：2007-04-27

    申请人： Georges Harik ,  Noam Shazeer

    发明人： Georges Harik ,  Noam Shazeer

    IPC分类号： G06F17/21

    CPC分类号： G06F17/30684 ,  G06F17/27

    摘要： One embodiment of the present invention provides a system that learns a generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and cluster nodes representing clusters of conceptually related words. Within the current model, nodes are coupled together by weighted links, so that if a cluster node in the probabilistic model fires, a weighted link from the cluster node to another node causes the other node to fire with a probability proportionate to the link weight. The system also receives a set of training documents, wherein each training document contains a set of words. Next, the system applies the set of training documents to the current model to produce a new model.

    Method of spell-checking search queries
    7.
    发明授权
    Method of spell-checking search queries 有权
    拼写检查搜索查询的方法

    公开(公告)号：US08051374B1

    公开(公告)日：2011-11-01

    申请号：US11670885

    申请日：2007-02-02

    申请人： Noam Shazeer

    发明人： Noam Shazeer

    IPC分类号： G06F17/21

    CPC分类号： G06F17/273 ,  Y10S707/99933 ,  Y10S707/99936

    摘要： A computer-implemented method for determining whether a target text-string is correctly spelled is provided. The target text-string is compared to a corpus to determine a set of contexts which each include an occurrence of the target text-string. Using heuristics, each context of the set is characterized based on occurrences in the corpus of the target text-string and a reference text-string. Contexts are characterized as including a correct spelling of the target text-string, an incorrect spelling of the reference text-string, or including an indeterminate usage of the target text-string. A likelihood that the target text-string is a misspelling of the reference text-string is computed as a function of the quantity of contexts including a correct spelling of the target text-string and the quantity of contexts including an incorrect spelling of a reference text-string. In one application, the target text-string is received in a search query, the search executed following a spell-check.

    摘要翻译： 提供了一种用于确定目标文本串是否正确拼写的计算机实现的方法。 将目标文本字符串与语料库进行比较，以确定一组上下文，每个上下文包括目标文本字符串的出现。 使用启发式，集合的每个上下文基于目标文本字符串和参考文本字符串的语料库中的出现来表征。 上下文的特征在于包括目标文本字符串的正确拼写，引用文本字符串的错误拼写，或包含目标文本字符串的不确定使用。 目标文本字符串是引用文本字符串拼写错误的可能性是作为上下文数量的函数计算的，包括目标文本字符串的正确拼写和上下文的数量，包括引用文本的拼写错误 -串。 在一个应用程序中，目标文本字符串在搜索查询中被接收，搜索在拼写检查之后执行。

    Scaling machine learning using approximate counting
    8.
    发明授权
    Scaling machine learning using approximate counting 有权
    缩放机器学习使用近似计数

    公开(公告)号：US08019704B1

    公开(公告)日：2011-09-13

    申请号：US12778877

    申请日：2010-05-12

    申请人： Simon Tong ,  Noam Shazeer

    发明人： Simon Tong ,  Noam Shazeer

    IPC分类号： G06F15/18

    CPC分类号： G06N99/005

    摘要： A system may track statistics for a number of features using an approximate counting technique by: subjecting each feature to multiple, different hash functions to generate multiple, different hash values, where each of the hash values may identify a particular location in a memory, and storing statistics for each feature at the particular locations identified by the hash values. The system may generate rules for a model based on the tracked statistics.

    摘要翻译： 系统可以使用近似计数技术来跟踪多个特征的统计量：通过以下方式对每个特征进行多个不同的散列函数生成多个不同的哈希值，其中每个散列值可以标识存储器中的特定位置，以及 在由哈希值标识的特定位置处存储每个特征的统计信息。 系统可以根据跟踪的统计信息为模型生成规则。