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    • 4. 发明授权
    • Enhanced matching through explore/exploit schemes
    • 通过探索/利用方案增强匹配
    • US08560293B2
    • 2013-10-15
    • US13569728
    • 2012-08-08
    • H. Scott RoyRaghunath RamakrishnanPradheep ElangoNitin MotgiDeepak K. AgarwalWei ChuBee-Chung Chen
    • H. Scott RoyRaghunath RamakrishnanPradheep ElangoNitin MotgiDeepak K. AgarwalWei ChuBee-Chung Chen
    • G06F17/50
    • G06F17/3089
    • Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.
    • 内容项被选择以在门户页面上显示,以便最大化诸如点击率的性能度量。 解决与内容选择相关的问题,例如改变内容池,可变性能度量,以及一旦项目已被显示给用户,对项目的反馈的延迟。 用于多武装强盗问题的基于优先权的方案的适应性用于预测未来数据趋势。 适应性将关于未来时间段的实验引入计算,这增加了解决多武装强盗问题的数据集。 此外,贝叶斯探索/漏洞利用方法被制定为一个优化问题,解决门户页面的内容项目选择的所有问题。 该优化问题由拉格朗日弛豫和正态逼近法进行修正,可实时计算优化问题。
    • 5. 发明授权
    • Enhanced matching through explore/exploit schemes
    • 通过探索/利用方案增强匹配
    • US08244517B2
    • 2012-08-14
    • US12267534
    • 2008-11-07
    • H. Scott RoyRaghunath RamakrishnanPradheep ElangoNitin MotgiDeepak K. AgarwalWei ChuBee-Chung Chen
    • H. Scott RoyRaghunath RamakrishnanPradheep ElangoNitin MotgiDeepak K. AgarwalWei ChuBee-Chung Chen
    • G06F9/45
    • G06F17/3089
    • Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.
    • 内容项被选择以在门户页面上显示,以便最大化诸如点击率的性能度量。 解决与内容选择相关的问题,例如改变内容池,可变性能度量,以及一旦项目已被显示给用户,对项目的反馈的延迟。 用于多武装强盗问题的基于优先权的方案的改编用于预测未来数据趋势。 适应性将关于未来时间段的实验引入计算,这增加了解决多武装强盗问题的数据集。 此外,贝叶斯探索/漏洞利用方法被制定为一个优化问题,解决门户页面的内容项目选择的所有问题。 该优化问题由拉格朗日弛豫和正态逼近法进行修正,可实时计算优化问题。
    • 7. 发明申请
    • ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES
    • 通过探索/开发计划进行更好的匹配
    • US20120303349A1
    • 2012-11-29
    • US13569728
    • 2012-08-08
    • H. Scott RoyRaghunath RamakrishnanPradheep ElangoNitin MotgiDeepak K. AgarwalWei ChuBee-Chung Chen
    • H. Scott RoyRaghunath RamakrishnanPradheep ElangoNitin MotgiDeepak K. AgarwalWei ChuBee-Chung Chen
    • G06G7/62
    • G06F17/3089
    • Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.
    • 内容项被选择以在门户页面上显示,以便最大化诸如点击率的性能度量。 解决与内容选择相关的问题,例如改变内容池,可变性能度量,以及一旦项目已被显示给用户,对项目的反馈的延迟。 用于多武装强盗问题的基于优先权的方案的适应性用于预测未来数据趋势。 适应性将关于未来时间段的实验引入计算,这增加了解决多武装强盗问题的数据集。 此外,贝叶斯探索/漏洞利用方法被制定为一个优化问题,解决门户页面的内容项目选择的所有问题。 该优化问题由拉格朗日弛豫和正态逼近法进行修正,可实时计算优化问题。
    • 9. 发明申请
    • SOCIALIZED DEALS AND COUPONS
    • 社会保障和担保
    • US20130262243A1
    • 2013-10-03
    • US13436735
    • 2012-03-30
    • Nitin MotgiAmit MotgiBruce Ng
    • Nitin MotgiAmit MotgiBruce Ng
    • G06Q30/02
    • G06Q30/0207
    • Techniques for socialized commercial incentives are provided. A commercial incentive is displayed in a page displayed by a browser. A user interface element is displayed in association with the commercial incentive that enables a function to be performed with respect to the commercial incentive. The user interface element is determined to have been interacted with by a user. In response, the function associated with the commercial incentive is performed. The function may be a saving of the commercial incentive to a list of commercial incentives for the user, a displaying of the list of commercial incentives, a generating of a digitized version of the commercial incentive, or may be another function.
    • 提供社会化商业激励措施。 商业激励显示在浏览器显示的页面中。 与商业激励相关联地显示用户界面元素,其使得能够针对商业激励来执行功能。 用户界面元素被确定为已被用户交互。 作为回应,执行与商业激励相关的功能。 该功能可能是将商业激励的挽救放在用户的商业激励列表中,显示商业激励的列表,产生商业激励的数字化版本,或者可以是另一个功能。