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    • 3. 发明授权
    • Selecting an algorithm for identifying similar user identifiers based on predicted click-through-rate
    • 基于预测的点击率选择用于识别类似用户标识符的算法
    • US08886575B1
    • 2014-11-11
    • US13534480
    • 2012-06-27
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • G06F15/18
    • G06Q30/0201
    • A computerized method, system for, and computer-readable medium operable to select an algorithm for generating models configured to identify similar user identifiers. A first plurality of models generated by a first algorithm is received. A plurality of lists of similar user identifiers is generated. User queries associated with user identifiers on the plurality of lists of similar user identifiers are identified. Predicted click-through rates for the user queries is received. An average predicted click-through rate is computed for each model based on the predicted click-through rates. A weighted average predicted click-through rate associated with the first plurality of models is computed. The weighted average predicted click-through rate for the first plurality of models can be compared to a weighted average predicted click-through rate for a second plurality of models generated by a second algorithm. The algorithm for generating models is selected based on the comparison.
    • 一种计算机化方法,系统和计算机可读介质,其可操作以选择用于生成被配置为识别相似用户标识符的模型的算法。 接收由第一算法生成的第一多个模型。 生成多个相似用户标识符的列表。 识别与多个相似用户标识符列表上的用户标识符相关联的用户查询。 收到用户查询的预测点击率。 根据预测的点击率计算每个模型的平均预测点击率。 计算与第一多个模型相关联的加权平均预测点击率。 可以将第一多个模型的加权平均预测点击率与由第二算法生成的第二多个模型的加权平均预测点击率进行比较。 基于比较选择生成模型的算法。
    • 4. 发明授权
    • Selecting keywords using co-visitation information
    • 使用共同访问信息选择关键字
    • US08572096B1
    • 2013-10-29
    • US13297544
    • 2011-11-16
    • Rohan SethShumeet BalujaDandapani SivakumarDeepak Ravichandran
    • Rohan SethShumeet BalujaDandapani SivakumarDeepak Ravichandran
    • G06F17/30
    • G06F17/30867G06Q10/10
    • Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting keywords for resources. In one aspect, a method includes identifying a particular online resource that includes non-text content. Co-visitation data are obtained for the particular resource. The co-visitation data specify one or more co-requested online resources for the particular online resource. Each of the co-requested online resources were requested by a user device within a threshold period of the request for the particular online resource by the user device. Keywords are identified for each of the co-requested online resources, and can include keywords that were selected based on text content of the co-requested online resource. One or more of the identified keywords are selected as keywords for the particular resource.
    • 方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于选择资源的关键字。 一方面,一种方法包括识别包括非文本内容的特定在线资源。 获取特定资源的共访问数据。 共同访问数据指定特定在线资源的一个或多个共同请求的在线资源。 在用户设备对特定在线资源的请求的阈值期间内,由用户设备请求每个共同请求的在线资源。 针对每个共同请求的在线资源标识关键字,并且可以包括基于共同请求的在线资源的文本内容而选择的关键字。 选择一个或多个所识别的关键词作为特定资源的关键字。
    • 5. 发明授权
    • Geographically local query detection
    • 地理位置查询检测
    • US09424342B1
    • 2016-08-23
    • US12708583
    • 2010-02-19
    • Deepak RavichandranDandapani SivakumarRohan SethShumeet Baluja
    • Deepak RavichandranDandapani SivakumarRohan SethShumeet Baluja
    • G06F17/30
    • G06F17/30637G06F17/3087
    • Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting local search queries. In one aspect, a method includes accessing a search query log that includes data specifying search queries corresponding to particular geographic regions and for at least one of the search queries corresponding to the particular geographic region generating a geo-query count that represents a total number of times that the search query was received over a specified period. The geo-query count is compared to a corresponding expected query count for the search query, where the expected query count is a baseline number of times that the query is expected to be received. In response to determining that the search query has a geo-query count that exceeds the corresponding expected query count by at least a threshold amount, the particular query is classified as a local query for the particular geographic region.
    • 方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于检测本地搜索查询。 一方面,一种方法包括访问包括指定与特定地理区域相对应的搜索查询的数据的搜索查询日志,以及对应于特定地理区域的搜索查询中的至少一个生成地理查询计数,所述地理查询计数表示总数 在指定时间段内收到搜索查询的时间。 将地理查询计数与搜索查询的相应预期查询计数进行比较,其中预期查询计数是希望接收查询的基准次数。 响应于确定搜索查询具有超过相应的预期查询计数至少阈值量的地理查询计数,特定查询被分类为特定地理区域的本地查询。
    • 7. 发明授权
    • Device identifier similarity models derived from online event signals
    • 从在线事件信号导出的设备标识符相似性模型
    • US09065727B1
    • 2015-06-23
    • US13601775
    • 2012-08-31
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • G06F15/173H04L12/26
    • H04L43/04G06Q30/06H04L67/22
    • A computerized method and system operable to build a device identifier similarity model with online event signals and determine similar network device identifiers. A processing circuit receives a first set of network device identifiers. The processing circuit represents each network device identifier of the first set by feature data associated with each network device identifier's network activity, where the feature data is associated with the content clicked-on or converted-on. The processing circuit applies abstractions on the feature data to form concepts. The processing circuit derives at least one hierarchy of feature data based on the keywords and concepts of the feature data. The processing circuit expands the feature data based on the derived at least one hierarchy of feature data and generates the device identifier similarity model based on the expanded feature data. The processing circuit is also capable of determining long-term and short-term history events.
    • 一种计算机化方法和系统,其可操作以用在线事件信号构建设备标识符相似性模型并确定类似的网络设备标识符。 处理电路接收第一组网络设备标识符。 处理电路通过与每个网络设备标识符的网络活动相关联的特征数据来表示第一组的每个网络设备标识符,其中特征数据与点击或转换的内容相关联。 处理电路对特征数据应用抽象以形成概念。 处理电路根据特征数据的关键词和概念,产生至少一个特征数据层级。 处理电路基于导出的特征数据的至少一个层次扩展特征数据,并且基于扩展的特征数据生成设备标识符相似性模型。 处理电路还能够确定长期和短期历史事件。
    • 8. 发明授权
    • Selecting a list of network user identifiers based on long-term and short-term history data
    • 根据长期和短期历史数据选择网络用户标识符列表
    • US08527526B1
    • 2013-09-03
    • US13462130
    • 2012-05-02
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • G06F7/00
    • G06Q30/0277
    • A computerized method, system for, and computer-readable medium operable to select a list of network user identifiers. A processing circuit receives a list of network user identifiers represented by long-term history data indicative of web pages visited prior to a first time and short-term history data indicative of web pages visited after the first time and prior to a second time. Long-term interest categories, corresponding weight values for each long-term interest category, short-term interest categories and corresponding weight values for each short-term interest category are identified. A model comprising the long-term and short-term interest categories is generated based on the weight values of the long-term and short-term interest categories using either arithmetic or harmonic progression. The processing circuit receives a list of candidate network user identifiers and generates a list of similar network user identifiers based on the model.
    • 用于选择网络用户标识符列表的计算机化方法,系统和计算机可读介质。 处理电路接收由指示在第一时间之前访问的网页的长期历史数据表示的网络用户标识符的列表,以及指示在第一次和第二次之前访问的网页的短期历史数据。 确定每个短期利益类别的长期利益类别,每个长期利益类别的相应权重值,短期利益类别和相应的权重值。 包括长期和短期兴趣类别的模型是基于使用算术或谐波进行的长期和短期兴趣类别的权重值生成的。 处理电路接收候选网络用户标识符的列表,并且基于该模型生成类似的网络用户标识符的列表。
    • 10. 发明授权
    • Adjust similar users identification based on performance feedback
    • 根据性能反馈调整类似的用户识别
    • US08874589B1
    • 2014-10-28
    • US13550073
    • 2012-07-16
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • Jia LiuYijian BaiManojav PatilDeepak RavichandranSittichai JiampojamarnShankar Ponnekanti
    • G06F17/30
    • G06F17/30867
    • A method of setting a threshold similarity score value for a first plurality of network user identifiers. The first plurality of network user identifiers, a second plurality of network user identifiers and characteristic data associated with the network user identifiers is received. A performance target and an experimental threshold similarity score value are designated. A similarity score between the first and second plurality of network user identifiers is calculated. Performance statistics data for each of the second plurality of network user identifiers having a similarity score greater than or equal to the experimental threshold similarity score value is collected and compared to the similarity score of the network user identifier. Based on the comparison, the experimental threshold similarity score value is adjusted to a similarity score value that achieves the performance target and the threshold similarity score value is set to the adjusted experimental threshold similarity score value.
    • 一种为第一多个网络用户标识符设置阈值相似度得分值的方法。 接收第一多个网络用户标识符,第二多个网络用户标识符和与网络用户标识符相关联的特征数据。 指定性能目标和实验阈值相似性得分值。 计算第一和第二多个网络用户标识符之间的相似度分数。 收集具有大于或等于实验阈值相似性得分值的相似度得分的第二多个网络用户标识符中的每一个的性能统计数据,并与网络用户标识符的相似性得分进行比较。 基于比较,将实验阈值相似性得分值调整为达到性能目标的相似性得分值,并将阈值相似性得分值设置为调整后的实验阈值相似性得分值。