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    Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking
    4.
    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。

    Framework to evaluate content display policies
    5.
    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
    6.
    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。

    Determining User Preference of Items Based on User Ratings and User Features
    7.
    发明申请
    Determining User Preference of Items Based on User Ratings and User Features 有权
    基于用户评分和用户特征确定用户偏好

    公开(公告)号：US20100250556A1

    公开(公告)日：2010-09-30

    申请号：US12416036

    申请日：2009-03-31

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

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

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30699

    摘要： 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.

    摘要翻译： 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。

    Determining user preference of items based on user ratings and user features
    8.
    发明授权
    Determining user preference of items based on user ratings and user features 有权
    根据用户评分和用户特征确定项目的用户偏好

    公开(公告)号：US08301624B2

    公开(公告)日：2012-10-30

    申请号：US12416036

    申请日：2009-03-31

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

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

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30699

    摘要： 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.

    摘要翻译： 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。

    METHOD AND SYSTEM FOR BIDDING ON ADVERTISEMENTS
    9.
    发明申请
    METHOD AND SYSTEM FOR BIDDING ON ADVERTISEMENTS 审中-公开
    招标广告的方法和制度

    公开(公告)号：US20080114672A1

    公开(公告)日：2008-05-15

    申请号：US11734294

    申请日：2007-04-12

    申请人： Sihem Amer Yahia ,  Raghu Ramakrishnan ,  Michael Schwarz ,  Jayavel Shanmugasundaram ,  Andrew Tomkins

    发明人： Sihem Amer Yahia ,  Raghu Ramakrishnan ,  Michael Schwarz ,  Jayavel Shanmugasundaram ,  Andrew Tomkins

    IPC分类号： G06Q40/00

    CPC分类号： G06Q30/02 ,  G06Q40/04

    摘要： A system and method for bidding on advertisements. The system includes a query engine and an advertisement engine. The query engine receives a query from the user. The query engine analyzes the query to determine a query intent that is matched to a predetermined domain. A translated query is generated including the domain type. Once a domain is selected, the query may be further analyzed to determine generic domain information. The domain and associated information may then be matched to a list of advertisements. The advertisement may be assigned a score based on a bid price and a quality of the advertisement.

    摘要翻译： 广告投标系统和方法。 该系统包括查询引擎和广告引擎。 查询引擎从用户接收查询。 查询引擎分析查询以确定与预定域匹配的查询意图。 生成包含域类型的翻译查询。 一旦选择了域，则可以进一步分析查询以确定通用域信息。 然后可以将域和相关联的信息与广告列表进行匹配。 该广告可以基于投标价格和广告的质量而被分配。

    System for generating advertisements based on search intent
    10.
    发明申请
    System for generating advertisements based on search intent 审中-公开
    基于搜索意图生成广告的系统

    公开(公告)号：US20080114607A1

    公开(公告)日：2008-05-15

    申请号：US11595585

    申请日：2006-11-09

    申请人： Sihem Amer-Yahia ,  Lin Guo ,  Raghu Ramakrishnan ,  Jayavel Shanmugasundaram ,  Utkarsh Srivastava ,  Andrew Tomkins

    发明人： Sihem Amer-Yahia ,  Lin Guo ,  Raghu Ramakrishnan ,  Jayavel Shanmugasundaram ,  Utkarsh Srivastava ,  Andrew Tomkins

    IPC分类号： G06Q99/00

    CPC分类号： G06Q30/02 ,  G06Q30/0241

    摘要： A system and method for generating advertisements based on search intent. The system includes a query engine, and an advertisement engine. The query engine receives a query from the user. The query engine analyzes the query to determine a query intent that is matched to a predetermined domain. A translated query is generated including the domain type. Once a domain is selected, the query may be further analyzed to determine generic domain information. The domain and associated information may then be matched to a list of advertisements. The advertisement may be assigned an ad match score based on a correlation between the query information and various listing information provided in the advertisement.

    摘要翻译： 一种基于搜索意图产生广告的系统和方法。 该系统包括查询引擎和广告引擎。 查询引擎从用户接收查询。 查询引擎分析查询以确定与预定域匹配的查询意图。 生成包含域类型的翻译查询。 一旦选择了域，则可以进一步分析查询以确定通用域信息。 然后可以将域和相关联的信息与广告列表进行匹配。 可以基于查询信息和广告中提供的各种列表信息之间的相关性，向广告分配广告匹配分数。