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公开(公告)号：US20150057035A1
公开(公告)日：2015-02-26
申请号：US13973430
申请日：2013-08-22
Applicant: YAHOO! INC.
Inventor： Oren Shlomo Somekh , Natalia Aizenberg , Michal Aharon
CPC classification number: H04W4/12 , H04L51/14 , H04L51/32 , H04L67/26 , H04W4/02 , H04W4/021 , H04W4/023 , H04W4/06
Abstract: Disclosed is a system and method for selectively delivering shared information. The disclosed systems and methods enable a sending user to decide what context of information is to be shared, in that a sending user can determine when and where information is shared. This enables increased social engagement towards relevant information. Additionally, this enables a vast opportunity for commercial opportunities, whereby advertisements can be served contingent upon a consumer's context, and/or only when the product or service offered to the consumer is relevant.
Abstract translation: 公开了一种用于选择性地传送共享信息的系统和方法。 所公开的系统和方法使得发送用户能够决定要共享什么上下文信息，因为发送用户可以确定信息共享的时间和地点。 这可以增加社会参与相关信息。 此外，这使得商业机会获得巨大的机会，由此广告可以根据消费者的情境和/或仅当提供给消费者的产品或服务是相关的而被服务时。
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公开(公告)号：US20170177337A1
公开(公告)日：2017-06-22
申请号：US14979085
申请日：2015-12-22
Applicant: Yahoo! Inc.
Inventor： Shahar Golan , Oren Shlomo Somekh , Michal Aharon
CPC classification number: G06Q30/0277 , G06F16/2237 , G06F16/2455 , G06F16/248
Abstract: Briefly, embodiments disclosed herein may relate to digital content selection, and more particularly to weighted pseudo-random digital content selection for use in and/or with online digital content delivery, such as online advertising, for example.
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3.发明授权Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization 有权通过矩阵分解学习潜在因子模型的有效和容错分布式算法公开(公告)号：US09535938B2
公开(公告)日：2017-01-03
申请号：US14123259
申请日：2013-03-15
Applicant: Yahoo! Inc.
Inventor： Oren Shlomo Somekh , Edward Bornikov , Nadav Golbandi , Oleg Rokhlenko , Ronny Lempel
CPC classification number: G06F17/30312 , G06Q30/0631 , G09B19/00
Abstract: A method for estimating model parameters. The method comprises receiving a data set related to a plurality of users and associated content, partitioning the data set into a plurality of sub data sets in accordance with the users so that data associated with each user are not partitioned into more than one sub data set, storing each of the sub data sets in a separate one of a plurality of user data storages, each of said data storages being coupled with a separate one of a plurality of estimators, storing content associated with the plurality of users in a content storage, where the content storage is coupled to the plurality of estimators so that the content in the content storage is shared by the estimators, and estimating, asynchronously by each estimator, one or more parameters associated with a model based on data from one of the sub data sets.
Abstract translation: 一种估计模型参数的方法。 该方法包括：接收与多个用户相关的数据集和相关联的内容，根据用户将数据集划分成多个子数据集，使得与每个用户相关联的数据不被划分成多个子数据集 将每个子数据集存储在多个用户数据存储器中的单独一个中，每个所述数据存储器与多个估计器中的单独一个估计器耦合，将与多个用户相关联的内容存储在内容存储器中， 其中所述内容存储器耦合到所述多个估计器，使得所述内容存储器中的内容由所述估计器共享，并且由每个估计器异步地估计与来自所述子数据之一的数据的与模型相关联的一个或多个参数 套。
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Patent Agency Ranking
公开(公告)号：US20170083522A1
公开(公告)日：2017-03-23
申请号：US14857258
申请日：2015-09-17
Applicant: Yahoo! Inc.
Inventor： Oren Shlomo Somekh , Michal Aharon , Shahar Golan , Noa Avigdor-Elgrabli , Dana Drachsler Cohen
IPC: G06F17/30 , G06F3/0484 , G06N99/00
CPC classification number: G06F17/3053 , G06F3/04842 , G06F17/30324 , G06F17/30867 , G06N99/005
Abstract: Systems and methods for building a latent item vector and item bias for a new item in a collaborative filtering system are disclosed. The method includes dividing incoming users into intervals with each interval having a learning phase and a selection phase. The learning phase scores each incoming user according to a best estimate latent vector and bias and saves the highest score. In the selection each incoming user is scored and a user exceeding the highest score is selected. The best estimate latent vector and bias is then updated based on the user's vector and bias, and the user's interaction with the item. The updated best estimate latent vector is then used in further intervals for learning and selecting users.
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5.发明申请公开(公告)号：US20140280251A1
公开(公告)日：2014-09-18
申请号：US14123321
申请日：2013-03-15
Applicant: YAHOO! Inc.
Inventor： Oren Shlomo Somekh , Nadav Golbandi , Oleg Rokhlenko , Ronny Lempel
IPC: G06F17/30
CPC classification number: G06F17/30867 , G06Q30/0269
Abstract: A method for adjusting one or more parameters associated with a model. The method comprises obtaining, from a first source, first information related to activity of a user. The method further comprises adjusting one or more parameters associated with a model based on the first information collected within a first length of time, and obtaining, from a second source, second information related to activity of the user. The method further comprises adjusting the one or more parameters associated with the model based on the second information collected within a second length of time and a measure indicative of performance of the model, wherein the second length of time is larger than the first length of time.
Abstract translation: 一种用于调整与模型相关联的一个或多个参数的方法。 该方法包括从第一来源获得与用户的活动相关的第一信息。 该方法还包括基于在第一时间长度内收集的第一信息调整与模型相关联的一个或多个参数，以及从第二源获得与用户的活动相关的第二信息。 该方法还包括基于在第二时间长度内收集的第二信息和指示模型性能的量度来调整与模型相关联的一个或多个参数，其中第二时间长度大于第一时间长度 。
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6.发明授权公开(公告)号：US09348924B2
公开(公告)日：2016-05-24
申请号：US14123321
申请日：2013-03-15
Applicant: Yahoo! Inc.
Inventor： Oren Shlomo Somekh , Nadav Golbandi , Oleg Rokhlenko , Ronny Lempel
CPC classification number: G06F17/30867 , G06Q30/0269
Abstract: A method for adjusting one or more parameters associated with a model. The method comprises obtaining, from a first source, first information related to activity of a user. The method further comprises adjusting one or more parameters associated with a model based on the first information collected within a first length of time, and obtaining, from a second source, second information related to activity of the user. The method further comprises adjusting the one or more parameters associated with the model based on the second information collected within a second length of time and a measure indicative of performance of the model, wherein the second length of time is larger than the first length of time.
Abstract translation: 一种用于调整与模型相关联的一个或多个参数的方法。 该方法包括从第一来源获得与用户的活动相关的第一信息。 该方法还包括基于在第一时间长度内收集的第一信息调整与模型相关联的一个或多个参数，以及从第二来源获得与用户活动相关的第二信息。 该方法还包括基于在第二时间长度内收集的第二信息和指示模型性能的量度来调整与模型相关联的一个或多个参数，其中第二时间长度大于第一时间长度 。
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公开(公告)号：US20160110646A1
公开(公告)日：2016-04-21
申请号：US14519273
申请日：2014-10-21
Applicant: Yahoo! Inc.
Inventor： Oren Shlomo Somekh , Shahar Golan , Nadav Golbandi , Zohar Karnin , Oleg Rokhlenko , Oren Anava , Ronny Lempel
Abstract: Method, system, and programs for estimating interests of a plurality of users with respect to a new piece of information are disclosed. In one example, historical interests of the plurality of users are obtained with respect to one or more existing pieces of information. One or more users are selected from the plurality of users. Historical interests of the one or more users can minimize an objective function over the plurality of users. Interests of the one or more users are obtained with respect to the new piece of information. Estimated interests of the plurality of users are generated with respect to the new piece of information based on the obtained interests of the one or more users.
Abstract translation: 公开了用于估计关于新信息的多个用户的兴趣的方法，系统和程序。 在一个示例中，针对一个或多个现有的信息获得多个用户的历史兴趣。 从多个用户中选择一个或多个用户。 一个或多个用户的历史兴趣可以使多个用户的目标功能最小化。 获得关于新的信息的一个或多个用户的兴趣。 基于获得的一个或多个用户的兴趣，针对新的信息片段生成多个用户的估计兴趣。
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公开(公告)号：US09191788B2
公开(公告)日：2015-11-17
申请号：US13973430
申请日：2013-08-22
Applicant: YAHOO! INC.
Inventor： Oren Shlomo Somekh , Natalia Aizenberg , Michal Aharon
CPC classification number: H04W4/12 , H04L51/14 , H04L51/32 , H04L67/26 , H04W4/02 , H04W4/021 , H04W4/023 , H04W4/06
Abstract: Disclosed is a system and method for selectively delivering shared information. The disclosed systems and methods enable a sending user to decide what context of information is to be shared, in that a sending user can determine when and where information is shared. This enables increased social engagement towards relevant information. Additionally, this enables a vast opportunity for commercial opportunities, whereby advertisements can be served contingent upon a consumer's context, and/or only when the product or service offered to the consumer is relevant.
Abstract translation: 公开了一种用于选择性地传送共享信息的系统和方法。 所公开的系统和方法使得发送用户能够决定要共享什么上下文信息，因为发送用户可以确定信息共享的时间和地点。 这可以增加社会参与相关信息。 此外，这使得商业机会获得巨大的机会，由此广告可以根据消费者的情境和/或仅当提供给消费者的产品或服务是相关的而被服务时。
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