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    • 1. 发明授权
    • Method and appartus for using B measures to learn balanced relevance functions from expert and user judgments
    • 使用B措施从专家和用户判断中学习平衡相关功能的方法和应用
    • US07685078B2
    • 2010-03-23
    • US11755134
    • 2007-05-30
    • Keke ChenYa ZhangZhaohui ZhengHongyuan ZhaGordon Sun
    • Keke ChenYa ZhangZhaohui ZhengHongyuan ZhaGordon Sun
    • G06N5/00
    • G06F17/30864
    • The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.
    • 本发明涉及用于确定内容项相关性功能的系统和方法。 该方法包括在搜索提供者处收集用户偏好数据以存储在用户偏好数据存储中,并在搜索提供商处收集专家判断数据以存储在专家样本数据存储中。 建模模块通过使用专家判断数据来训练基本模型,并通过使用用户偏好数据来调整基本模型,以学习一组或多个调谐模型。 测量(B测量)旨在评估调谐模型与专家判断和用户偏好的平衡性能。 建模模块从具有B测量的调谐模型生成或选择内容项相关性函数作为选择标准。
    • 2. 发明申请
    • SYSTEM AND METHOD FOR LEARNING BALANCED RELEVANCE FUNCTIONS FROM EXPERT AND USER JUDGMENTS
    • 从专家和用户判断中学习平衡相关函数的系统和方法
    • US20080301069A1
    • 2008-12-04
    • US11755134
    • 2007-05-30
    • Keke ChenYa ZhangZhaohui ZhengHongyuan ZhaGordon Sun
    • Keke ChenYa ZhangZhaohui ZhengHongyuan ZhaGordon Sun
    • G06F15/18
    • G06F17/30864
    • The present invention relates to systems and methods for determining a content item relevance function. The method comprises collecting user preference data at a search provider for storage in a user preference data store and collecting expert-judgment data at the search provider for storage in an expert sample data store. A modeling module trains a base model through the use of the expert-judgment data and tunes the base model through the use of the user preference data to learn a set of one or more tuned models. A measure (B measure) is designed to evaluate the balanced performance of tuned model over expert judgment and user preference. The modeling module generates or selects the content item relevance function from the tuned models with B measure as the selection criterion.
    • 本发明涉及用于确定内容项相关性功能的系统和方法。 该方法包括在搜索提供者处收集用户偏好数据以存储在用户偏好数据存储中,并在搜索提供商处收集专家判断数据以存储在专家样本数据存储中。 建模模块通过使用专家判断数据来训练基本模型,并通过使用用户偏好数据调整基本模型来学习一组或多个调谐模型。 测量(B测量)旨在评估调谐模型与专家判断和用户偏好的平衡性能。 建模模块从具有B测量的调谐模型生成或选择内容项相关性函数作为选择标准。
    • 3. 发明授权
    • System and method for cross domain learning for data augmentation
    • 用于数据增强的跨域学习的系统和方法
    • US08332334B2
    • 2012-12-11
    • US12566270
    • 2009-09-24
    • Bo LongBelle TsengSudarshan LamkhedeSrinivas VadrevuYa Zhang
    • Bo LongBelle TsengSudarshan LamkhedeSrinivas VadrevuYa Zhang
    • G06F15/18
    • G06N99/005H04L51/12
    • According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.
    • 根据示例性实施例,一种方法包括执行专用计算装置的指令,对于具有多个原始标签的标记源域数据,使用目标函数为标记的源域数据生成多个第一预测标签, 通过使用来自标记的目标域数据的多个标签确定目标函数。 该方法还包括由专用计算装置执行指令以对源域数据的第一预测标签和源域数据的原始标签应用标签关系函数,以确定用于标记的源域数据的多个权重因子 。 该方法还包括执行专用计算装置的指令,以使用标记的目标域数据,标记的源域数据和标记的源域数据的加权因子来生成新的目标函数,并评估新目标的性能 确定是否存在收敛的功能。
    • 5. 发明授权
    • Automated user behavior feedback system for whole page search success optimization
    • 自动用户行为反馈系统,用于整页搜索成功优化
    • US08832101B2
    • 2014-09-09
    • US12708499
    • 2010-02-18
    • David CiemiewiczYa ZhangBelle TsengJean-Marc Langlois
    • David CiemiewiczYa ZhangBelle TsengJean-Marc Langlois
    • G06F17/30
    • G06F17/30867
    • Various users' navigational behaviors relative to search results presented by a search engine are monitored. URLs that are visited and revised queries that are submitted after the submission of an original query are placed within a trail that begins with the original query. These trails are grouped based on the original queries with which they begin. For each trail group, a set of URLs that frequently occur in that group's trails, and a set of revised queries that frequently occur in that group's trails, are determined. These frequently occurring elements are mapped to the original queries with which all the trails in the corresponding trail group begin. In response to subsequent submissions of the same original query, the search engine ensures that URLs and revised queries that are mapped to the original query are prominently displayed on the search results pages that are initially returned in response to those submissions.
    • 监视与搜索引擎提供的搜索结果相关的各种用户的导航行为。 在提交原始查询后提交的访问和修改的查询的URL将放置在以原始查询开头的跟踪中。 这些路径根据他们开始的原始查询进行分组。 对于每个跟踪组,都会确定频繁发生在该组路径中的一组URL,以及经常发生在该组路径中的一组经过修改的查询。 这些经常出现的元素被映射到相应跟踪组中的所有路径开始的原始查询。 响应于相同原始查询的后续提交,搜索引擎确保映射到原始查询的URL和修改的查询显着地显示在最初返回以响应这些提交的搜索结果页面上。
    • 6. 发明申请
    • AUTOMATED USER BEHAVIOR FEEDBACK SYSTEM FOR WHOLE PAGE SEARCH SUCCESS OPTIMIZATION
    • 自动化用户行为反馈系统,用于全页搜索成功优化
    • US20110202522A1
    • 2011-08-18
    • US12708499
    • 2010-02-18
    • David CiemiewiczYa ZhangBelle TsengJean-Marc Langlois
    • David CiemiewiczYa ZhangBelle TsengJean-Marc Langlois
    • G06F17/30
    • G06F17/30867
    • Various users' navigational behaviors relative to search results presented by a search engine are monitored. URLs that are visited and revised queries that are submitted after the submission of an original query are placed within a trail that begins with the original query. These trails are grouped based on the original queries with which they begin. For each trail group, a set of URLs that frequently occur in that group's trails, and a set of revised queries that frequently occur in that group's trails, are determined. These frequently occurring elements are mapped to the original queries with which all the trails in the corresponding trail group begin. In response to subsequent submissions of the same original query, the search engine ensures that URLs and revised queries that are mapped to the original query are prominently displayed on the search results pages that are initially returned in response to those submissions.
    • 监视与搜索引擎提供的搜索结果相关的各种用户的导航行为。 在提交原始查询后提交的访问和修改的查询的URL将放置在以原始查询开头的跟踪中。 这些路径根据他们开始的原始查询进行分组。 对于每个跟踪组,都会确定频繁发生在该组路径中的一组URL,以及经常发生在该组路径中的一组经过修改的查询。 这些经常出现的元素被映射到相应跟踪组中的所有路径开始的原始查询。 响应于相同原始查询的后续提交,搜索引擎确保映射到原始查询的URL和修改的查询显着地显示在最初返回以响应这些提交的搜索结果页面上。