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    • 5. 发明授权
    • Framework to evaluate content display policies
    • 评估内容显示政策的框架
    • US08504558B2
    • 2013-08-06
    • US12184114
    • 2008-07-31
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • G06F17/30
    • G06Q30/02
    • 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.
    • 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。
    • 6. 发明申请
    • FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES
    • 评估内容显示政策的框架
    • US20100030717A1
    • 2010-02-04
    • US12184114
    • 2008-07-31
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • Deepak AgarwalPradheep ElangoRaghu RamakrishnanSeung-Taek ParkBee-Chung Chen
    • G06N5/02
    • G06Q30/02
    • 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.
    • 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。
    • 7. 发明申请
    • Determining User Preference of Items Based on User Ratings and User Features
    • 基于用户评分和用户特征确定用户偏好
    • US20100250556A1
    • 2010-09-30
    • US12416036
    • 2009-03-31
    • Seung-Taek ParkWei ChuTodd BeaupreDeepak K. AgarwalScott RoyRaghu Ramakrishnan
    • Seung-Taek ParkWei ChuTodd BeaupreDeepak K. AgarwalScott RoyRaghu Ramakrishnan
    • G06F17/30
    • G06F17/30699
    • A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user.
    • 基于协同过滤技术来确定用于多个项目的项目项目亲和度的集合。 确定基于项目项目亲和度的集合的项目的最近邻居项目的集合。 基于最小二乘回归确定用于多个项目和一组用户特征的一组用户特征项目亲和度。 部分基于用户特征项亲属度的集合来确定一组用户特征的最近邻居项目。 确定并存储每个项目和每个用户特征的最近邻项目的兼容关联权重。 基于特定用户的用户特征和特定用户消费的项目,包括用户的用户特征和用户消费的项目的最近邻项目的一组最近邻项目被识别为一组候选项,并且亲和度分数 确定候选项目。 至少部分地基于亲和度分数,向用户推荐来自该组候选项目的候选项目。
    • 8. 发明授权
    • Determining user preference of items based on user ratings and user features
    • 根据用户评分和用户特征确定项目的用户偏好
    • US08301624B2
    • 2012-10-30
    • US12416036
    • 2009-03-31
    • Seung-Taek ParkWei ChuTodd BeaupreDeepak K. AgarwalScott RoyRaghu Ramakrishnan
    • Seung-Taek ParkWei ChuTodd BeaupreDeepak K. AgarwalScott RoyRaghu Ramakrishnan
    • G06F17/30
    • G06F17/30699
    • A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user.
    • 基于协同过滤技术来确定用于多个项目的项目项目亲和度的集合。 确定基于项目项目亲和度的集合的项目的最近邻居项目的集合。 基于最小二乘回归确定用于多个项目和一组用户特征的一组用户特征项目亲和度。 部分基于用户特征项亲属度的集合来确定一组用户特征的最近邻居项目。 确定并存储每个项目和每个用户特征的最近邻项目的兼容关联权重。 基于特定用户的用户特征和特定用户消费的项目,包括用户的用户特征和用户消费的项目的最近邻项目的一组最近邻项目被识别为一组候选项,并且亲和度分数 确定候选项目。 至少部分地基于亲和度分数,向用户推荐来自该组候选项目的候选项目。