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1. 发明申请

US20080126303A1 System and method for identifying media content items and related media content items 有权
标题翻译：用于识别媒体内容项目和相关媒体内容项目的系统和方法
公开(公告)号：US20080126303A1
公开(公告)日：2008-05-29
申请号：US11518378
申请日：2006-09-07
申请人： Seung-Taek Park , David Myer Pennock
发明人： Seung-Taek Park , David Myer Pennock
IPC分类号： G06F17/30
CPC分类号： G06F17/30817 , G06F17/30828
摘要： A method comprising receiving a search query to generate a search result of one or more media items and providing a personalized search rank of the one or more media items on the basis of a user profile and an item relevance for a given media item with regard to the query metadata associated with the search result is identified and used to identify at least one related media item. The at least one related media item is ranked on the basis of the user profile and the metadata.
摘要翻译：一种方法，包括接收搜索查询以生成一个或多个媒体项目的搜索结果，并且基于用户简档和针对给定媒体项目的项目相关性提供所述一个或多个媒体项目的个性化搜索等级与搜索结果相关联的查询元数据被识别并用于识别至少一个相关媒体项目。基于用户简档和元数据对至少一个相关媒体项进行排名。

2. 发明授权

US08166029B2 System and method for identifying media content items and related media content items 有权
标题翻译：用于识别媒体内容项目和相关媒体内容项目的系统和方法
公开(公告)号：US08166029B2
公开(公告)日：2012-04-24
申请号：US11518378
申请日：2006-09-07
申请人： Seung-Taek Park , David Myer Pennock
发明人： Seung-Taek Park , David Myer Pennock
IPC分类号： G06F7/00
CPC分类号： G06F17/30817 , G06F17/30828
摘要： A method comprising receiving a search query to generate a search result of one or more media items and providing a personalized search rank of the one or more media items on the basis of a user profile and an item relevance for a given media item with regard to the query metadata associated with the search result is identified and used to identify at least one related media item. The at least one related media item is ranked on the basis of the user profile and the metadata.
摘要翻译：一种方法，包括接收搜索查询以生成一个或多个媒体项目的搜索结果，并且基于用户简档和针对给定媒体项目的项目相关性提供所述一个或多个媒体项目的个性化搜索等级与搜索结果相关联的查询元数据被识别并用于识别至少一个相关媒体项目。基于用户简档和元数据对至少一个相关媒体项进行排名。

3. 发明申请

US20100250556A1 Determining User Preference of Items Based on User Ratings and User Features 有权
标题翻译：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。

4. 发明申请

US20110107260A1 PREDICTING ITEM-ITEM AFFINITIES BASED ON ITEM FEATURES BY REGRESSION 有权
标题翻译：基于项目功能的预测项目活动
公开(公告)号：US20110107260A1
公开(公告)日：2011-05-05
申请号：US12613503
申请日：2009-11-05
申请人： Seung-Taek Park , Wei Chu , Wei Du , Uminder Singh , Jessi Dong
发明人： Seung-Taek Park , Wei Chu , Wei Du , Uminder Singh , Jessi Dong
IPC分类号： G06F3/048
CPC分类号： G06Q10/04
摘要： Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.
摘要翻译：确定两个项目与每个项目类似，不仅基于以前的实际用户行为，而且还基于所观察到的这两个项目的特征的相关性。如果选择具有第一特征的项目的高比例的用户也选择具有第二特征的项目，则第一特征和第二特征被确定为彼此具有一些亲和力，反之亦然。具有彼此具有高亲和力特征的两个项目被确定为彼此具有一些相似性，即使选择了这些项目中的一个的用户很少或没有选择其中一个项目。可以向已经选择第二项目的用户潜在地也对该用户感兴趣的用户推荐被确定为以这种方式与第二项目充分相似的第一项目。

5. 发明申请

US20100211568A1 PERSONALIZED RECOMMENDATIONS ON DYNAMIC CONTENT 有权
标题翻译：关于动态内容的个性化建议
公开(公告)号：US20100211568A1
公开(公告)日：2010-08-19
申请号：US12388941
申请日：2009-02-19
申请人： Wei Chu , Seung-Taek Park
发明人： Wei Chu , Seung-Taek Park
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： This disclosure describes systems and methods for selecting and/or ranking web-based content predicted to have the greatest interest to individual users. In particular, articles are ranked in terms of predicted interest for different users. This is done by optimizing an interest model and in particular through a method of bilinear regression and Bayesian optimization. The interest model is populated with data regarding users, the articles, and historical interest trends that types of users have expressed towards types of articles.
摘要翻译：本公开描述了用于选择和/或排名被预测为对个体用户最感兴趣的基于网络的内容的系统和方法。特别地，文章按不同用户的预期兴趣排列。这通过优化兴趣模型，特别是通过双线性回归和贝叶斯优化的方法来完成。兴趣模型填充有关用户，文章和用户对文章类型表达的历史兴趣趋势的数据。

6. 发明授权

US08301624B2 Determining user preference of items based on user ratings and user features 有权
标题翻译：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。

7. 发明申请

US20100125585A1 Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking 审中-公开
标题翻译：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。

8. 发明申请

US20100030717A1 FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES 有权
标题翻译：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。

9. 发明授权

US09600581B2 Personalized recommendations on dynamic content 有权
公开(公告)号：US09600581B2
公开(公告)日：2017-03-21
申请号：US12388941
申请日：2009-02-19
申请人： Wei Chu , Seung-Taek Park
发明人： Wei Chu , Seung-Taek Park
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： This disclosure describes systems and methods for selecting and/or ranking web-based content predicted to have the greatest interest to individual users. In particular, articles are ranked in terms of predicted interest for different users. This is done by optimizing an interest model and in particular through a method of bilinear regression and Bayesian optimization. The interest model is populated with data regarding users, the articles, and historical interest trends that types of users have expressed towards types of articles.

10. 发明授权

US08442929B2 Predicting item-item affinities based on item features by regression 有权
标题翻译：通过回归预测基于项目特征的项目项目亲和度
公开(公告)号：US08442929B2
公开(公告)日：2013-05-14
申请号：US12613503
申请日：2009-11-05
申请人： Seung-Taek Park , Wei Chu , Wei Du , Uminder Singh , Jessi Dong
发明人： Seung-Taek Park , Wei Chu , Wei Du , Uminder Singh , Jessi Dong
IPC分类号： G06F17/00 , G06N5/00
CPC分类号： G06Q10/04
摘要： Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.
摘要翻译：确定两个项目与每个项目类似，不仅基于以前的实际用户行为，而且还基于所观察到的这两个项目的特征的相关性。如果选择具有第一特征的项目的高比例的用户也选择具有第二特征的项目，则第一特征和第二特征被确定为彼此具有一些亲和力，反之亦然。具有彼此具有高亲和力特征的两个项目被确定为彼此具有一些相似性，即使选择了这些项目中的一个的用户很少或没有选择其中一个项目。可以向已经选择第二项目的用户潜在地也对该用户感兴趣的用户推荐被确定为以这种方式与第二项目充分相似的第一项目。

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