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    Framework to evaluate content display policies
    4.
    发明授权
    Framework to evaluate content display policies 有权
    评估内容显示政策的框架

    公开(公告)号：US08504558B2

    公开(公告)日：2013-08-06

    申请号：US12184114

    申请日：2008-07-31

    申请人： Deepak Agarwal ,  Pradheep Elango ,  Raghu Ramakrishnan ,  Seung-Taek Park ,  Bee-Chung Chen

    发明人： Deepak Agarwal ,  Pradheep Elango ,  Raghu Ramakrishnan ,  Seung-Taek Park ,  Bee-Chung Chen

    IPC分类号： G06F17/30

    CPC分类号： G06Q30/02

    摘要： Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.

    摘要翻译： 使用两种方法评估内容显示策略。 在第一种方法中，使用以“受控”的方式收集关于用户特征和内容特征的信息，生成真实模型。 模拟器会根据真实模型重播用户对门户网页的访问，并模拟与页面上的内容项目的交互。 各种指标用于比较不同的内容项目选择算法。 在第二种方法中，没有建立明确的真理模型。 受控服务计划的活动部分或全部重播; 内容项目选择算法学习使用观察到的用户活动。 衡量总体预测误差的度量用于比较不同的内容项目选择算法。 以受控方式收集的数据在这两种方法中起关键作用。

    FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES
    5.
    发明申请
    FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES 有权
    评估内容显示政策的框架

    公开(公告)号：US20100030717A1

    公开(公告)日：2010-02-04

    申请号：US12184114

    申请日：2008-07-31

    申请人： Deepak Agarwal ,  Pradheep Elango ,  Raghu Ramakrishnan ,  Seung-Taek Park ,  Bee-Chung Chen

    发明人： Deepak Agarwal ,  Pradheep Elango ,  Raghu Ramakrishnan ,  Seung-Taek Park ,  Bee-Chung Chen

    IPC分类号： G06N5/02

    CPC分类号： G06Q30/02

    摘要： Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.

    摘要翻译： 使用两种方法评估内容显示策略。 在第一种方法中，使用以“受控”的方式收集关于用户特征和内容特征的信息，生成真实模型。 模拟器会根据真实模型重播用户对门户网页的访问，并模拟与页面上的内容项目的交互。 各种指标用于比较不同的内容项目选择算法。 在第二种方法中，没有建立明确的真理模型。 受控服务计划的活动部分或全部重播; 内容项目选择算法学习使用观察到的用户活动。 衡量总体预测误差的度量用于比较不同的内容项目选择算法。 以受控方式收集的数据在这两种方法中起关键作用。

    ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES
    6.
    发明申请
    ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES 有权
    通过探索/开发计划进行更好的匹配

    公开(公告)号：US20120303349A1

    公开(公告)日：2012-11-29

    申请号：US13569728

    申请日：2012-08-08

    申请人： H. Scott Roy ,  Raghunath Ramakrishnan ,  Pradheep Elango ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

    发明人： H. Scott Roy ,  Raghunath Ramakrishnan ,  Pradheep Elango ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

    IPC分类号： G06G7/62

    CPC分类号： G06F17/3089

    摘要： 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.

    摘要翻译： 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。

    Enhanced matching through explore/exploit schemes
    7.
    发明授权
    Enhanced matching through explore/exploit schemes 有权
    通过探索/利用方案增强匹配

    公开(公告)号：US08560293B2

    公开(公告)日：2013-10-15

    申请号：US13569728

    申请日：2012-08-08

    申请人： H. Scott Roy ,  Raghunath Ramakrishnan ,  Pradheep Elango ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

    发明人： H. Scott Roy ,  Raghunath Ramakrishnan ,  Pradheep Elango ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

    IPC分类号： G06F17/50

    CPC分类号： G06F17/3089

    摘要： 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.

    摘要翻译： 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。

    Enhanced matching through explore/exploit schemes
    8.
    发明授权
    Enhanced matching through explore/exploit schemes 有权
    通过探索/利用方案增强匹配

    公开(公告)号：US08244517B2

    公开(公告)日：2012-08-14

    申请号：US12267534

    申请日：2008-11-07

    申请人： H. Scott Roy ,  Raghunath Ramakrishnan ,  Pradheep Elango ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

    发明人： H. Scott Roy ,  Raghunath Ramakrishnan ,  Pradheep Elango ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

    IPC分类号： G06F9/45

    CPC分类号： G06F17/3089

    摘要： 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.

    摘要翻译： 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。

    Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking
    10.
    发明申请
    Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking 审中-公开
    用于分段预测内容排名的双线性回归模型的联合分析

    公开(公告)号：US20100125585A1

    公开(公告)日：2010-05-20

    申请号：US12272607

    申请日：2008-11-17

    申请人： Wei Chu ,  Seung-Taek Park ,  Raghu Ramakrishnan ,  Bee-Chung Chen ,  Deepak K. Agarwal ,  Pradheep Elango ,  Scott Roy ,  Todd Beaupre

    发明人： Wei Chu ,  Seung-Taek Park ,  Raghu Ramakrishnan ,  Bee-Chung Chen ,  Deepak K. Agarwal ,  Pradheep Elango ,  Scott Roy ,  Todd Beaupre

    IPC分类号： G06F17/30 ,  G06F7/06 ,  G06F7/00

    CPC分类号： G06F16/3346 ,  G06F16/313

    摘要： Information with respect to users, items, and interactions between the users and items is collected. Each user is associated with a set of user features. Each item is associated with a set of item features. An expected score function is defined for each user-item pair, which represents an expected score a user assigns an item. An objective represents the difference between the expected score and the actual score a user assigns an item. The expected score function and the objective function share at least one common variable. The objective function is minimized to find best fit for some of the at least one common variable. Subsequently, the expected score function is used to calculate expected scores for individual users or clusters of users with respect to a set of items that have not received actual scores from the users. The set of items are ranked based on their expected scores.

    摘要翻译： 收集关于用户，项目以及用户和项目之间的交互的信息。 每个用户与一组用户特征相关联。 每个项目与一组项目特征相关联。 为每个用户 - 物品对定义预期分数函数，其表示用户分配项目的预期分数。 目标表示用户分配项目的预期分数与实际分数之间的差异。 预期得分函数和目标函数共享至少一个共同变量。 目标函数被最小化以找到最适合至少一个共同变量中的一些。 随后，使用预期分数函数来计算相对于尚未从用户那里获得实际分数的一组项目的个体用户或用户群的预期分数。 该组项目根据其预期分数进行排名。