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    • 4. 发明授权
    • System and method for detecting human judgment drift and variation control
    • 用于检测人类判断漂移和变异控制的系统和方法
    • US08005775B2
    • 2011-08-23
    • US12050436
    • 2008-03-18
    • Jesse BridgewaterLawrence Wai
    • Jesse BridgewaterLawrence Wai
    • G06F17/00G06N5/00
    • G06Q10/06
    • The present invention relates to methods, systems, and computer readable media comprising instructions for rescaling human judgment data for one or more items of content. The method of the present invention comprises generating one or more test sets comprising one or more items of content and generating one or more benchmark sets comprising one or more items of content common to each of the test sets. Judgment data for the one or more items of content comprising the one or more test sets from one or more human editors is received. A variation correction factor and a drift correction factor are identified for each of the one or more human editors. The variation correction factor and drift correction factor associated with each respective human editor are thereafter applied to the one or more items of content comprising the test set for which each human editor provided judgment data.
    • 本发明涉及包括用于将一个或多个内容项的人类判断数据重新定标的指令的方法,系统和计算机可读介质。 本发明的方法包括生成包括一个或多个内容项目的一个或多个测试集,并且生成包括每个测试集共同的一个或多个内容项目的一个或多个基准集合。 接收包含来自一个或多个人类编辑者的一个或多个测试集的一个或多个内容项目的判断数据。 为一个或多个人类编辑者中的每一个识别变异校正因子和漂移校正因子。 然后将与每个相应的人类编辑器相关联的变化校正因子和漂移校正因子应用于包括每个人类编辑器提供的判断数据的测试集的一个或多个内容项目。
    • 5. 发明授权
    • System and method for development of search success metrics
    • 用于开发搜索成功度量的系统和方法
    • US08024336B2
    • 2011-09-20
    • US12481196
    • 2009-06-09
    • Lawrence Wai
    • Lawrence Wai
    • G06F17/30
    • G06F17/3089
    • A system and method for development of search success metrics. A plurality of search engine result pages are collected and a target page success metric is determined for each page. A plurality of machine learned page success metrics are trained using a first subset of the search engine result pages and each result page's respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of search engine result pages. A predicted target page success metric is predicted for each of a second subset of the search engine result pages using each of the machine learned page success metrics. The accuracy of each of the machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages is then evaluated.
    • 用于开发搜索成功度量的系统和方法。 收集多个搜索引擎结果页面,并为每个页面确定目标页面成功度量。 使用搜索引擎结果页面和每个结果页面的相应目标页面成功度量的第一子集训练多个机器学习页面成功度量,其中训练每个机器学习页面成功度量以预测每个 的搜索引擎结果页面的第一个子集。 使用机器学习页面成功度量中的每一个来预测搜索引擎结果页面的第二子集中的每一个的预测目标页面成功度量。 然后评估在预测与搜索引擎结果页面的每个第二子集相关联的目标页面成功度量中的每个机器学习页面成功度量的准确性。
    • 6. 发明申请
    • SYSTEM AND METHOD FOR DEVELOPMENT OF SEARCH SUCCESS METRICS
    • 用于开发搜索成功度量的系统和方法
    • US20100312786A1
    • 2010-12-09
    • US12481196
    • 2009-06-09
    • Lawrence Wai
    • Lawrence Wai
    • G06F17/30G06F15/18
    • G06F17/3089
    • A system and method for development of search success metrics. A plurality of search engine result pages are collected and a target page success metric is determined for each page. A plurality of machine learned page success metrics are trained using a first subset of the search engine result pages and each result page's respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of search engine result pages. A predicted target page success metric is predicted for each of a second subset of the search engine result pages using each of the machine learned page success metrics. The accuracy of each of the machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages is then evaluated.
    • 用于开发搜索成功度量的系统和方法。 收集多个搜索引擎结果页面,并为每个页面确定目标页面成功度量。 使用搜索引擎结果页面和每个结果页面的相应目标页面成功度量的第一子集训练多个机器学习页面成功度量,其中训练每个机器学习页面成功度量以预测每个 的搜索引擎结果页面的第一个子集。 使用机器学习页面成功度量中的每一个来预测搜索引擎结果页面的第二子集中的每一个的预测目标页面成功度量。 然后评估在预测与搜索引擎结果页面的每个第二子集相关联的目标页面成功度量中的每个机器学习页面成功度量的准确性。
    • 7. 发明申请
    • SYSTEM AND METHOD FOR DETECTING HUMAN JUDGMENT DRIFT AND VARIATION CONTROL
    • 用于检测人员判断和变更控制的系统和方法
    • US20090240643A1
    • 2009-09-24
    • US12050436
    • 2008-03-18
    • Jesse BridgewaterLawrence Wai
    • Jesse BridgewaterLawrence Wai
    • G06F17/00
    • G06Q10/06
    • The present invention relates to methods, systems, and computer readable media comprising instructions for rescaling human judgment data for one or more items of content. The method of the present invention comprises generating one or more test sets comprising one or more items of content and generating one or more benchmark sets comprising one or more items of content common to each of the test sets. Judgment data for the one or more items of content comprising the one or more test sets from one or more human editors is received. A variation correction factor and a drift correction factor are identified for each of the one or more human editors. The variation correction factor and drift correction factor associated with each respective human editor are thereafter applied to the one or more items of content comprising the test set for which each human editor provided judgment data.
    • 本发明涉及包括用于将一个或多个内容项的人类判断数据重新定标的指令的方法,系统和计算机可读介质。 本发明的方法包括生成包括一个或多个内容项目的一个或多个测试集,并且生成包括每个测试集共同的一个或多个内容项目的一个或多个基准集合。 接收包含来自一个或多个人类编辑者的一个或多个测试集的一个或多个内容项目的判断数据。 为一个或多个人类编辑者中的每一个识别变异校正因子和漂移校正因子。 然后将与每个相应的人类编辑器相关联的变化校正因子和漂移校正因子应用于包括每个人类编辑器提供的判断数据的测试集的一个或多个内容项目。