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1. 发明授权

US08645298B2 Topic models 有权
标题翻译：主题模型
公开(公告)号：US08645298B2
公开(公告)日：2014-02-04
申请号：US12912428
申请日：2010-10-26
申请人： Philipp Hennig , David Stern , Thore Graepel , Ralf Herbrich
发明人： Philipp Hennig , David Stern , Thore Graepel , Ralf Herbrich
IPC分类号： G06F17/00 , G06F15/18 , G06N5/00 , G06F7/00 , G06F17/30
CPC分类号： G06N99/005 , G06N7/005
摘要： Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics.
摘要翻译：机器学习技术可用于训练计算设备以理解各种文档（例如，文本文件，网页，文章，电子表格等）。可以使用机器学习技术来解决计算设备可能缺乏用于理解这样的文档的人类智力的问题，例如其语义意义。因此，主题模型可以通过顺序处理文档和/或其特征（例如，文档作者，作者的地理位置，创作日期，作者的社交网络信息和/或文档元数据）来进行培训。另外，如本文所提供的，主题模型可以用于预测词，特征，文档和/或文档语料库例如表示特定主题的概率。

2. 发明申请

US20120101965A1 TOPIC MODELS 有权
标题翻译：主题模型
公开(公告)号：US20120101965A1
公开(公告)日：2012-04-26
申请号：US12912428
申请日：2010-10-26
申请人： Philipp Hennig , David Stern , Thore Graepel , Ralf Herbrich
发明人： Philipp Hennig , David Stern , Thore Graepel , Ralf Herbrich
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06N99/005 , G06N7/005
摘要： Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics.
摘要翻译：机器学习技术可用于训练计算设备以理解各种文档（例如，文本文件，网页，文章，电子表格等）。可以使用机器学习技术来解决计算设备可能缺乏用于理解这样的文档的人类智力的问题，例如其语义意义。因此，主题模型可以通过顺序处理文档和/或其特征（例如，文档作者，作者的地理位置，创作日期，作者的社交网络信息和/或文档元数据）来进行培训。另外，如本文所提供的，主题模型可以用于预测词，特征，文档和/或文档语料库例如表示特定主题的概率。

3. 发明授权

US08781915B2 Recommending items to users utilizing a bi-linear collaborative filtering model 有权
标题翻译：使用双线性协同过滤模型向用户推荐项目
公开(公告)号：US08781915B2
公开(公告)日：2014-07-15
申请号：US12253854
申请日：2008-10-17
申请人： Ralf Herbrich , Thore Graepel , David Stern
发明人： Ralf Herbrich , Thore Graepel , David Stern
IPC分类号： G06Q10/00
CPC分类号： G06Q30/0633 , G06Q30/02 , G06Q30/0203
摘要： A recommender system may be used to predict a user behavior that a user will give in relation to an item. In an embodiment such predictions are used to enable items to be recommended to users. For example, products may be recommended to customers, potential friends may be recommended to users of a social networking tool, organizations may be recommended to automated users or other items may be recommended to users. In an embodiment a memory stores a data structure specifying a bi-linear collaborative filtering model of user behaviors. In the embodiment an automated inference process may be applied to the data structure in order to predict a user behavior given information about a user and information about an item. For example, the user information comprises user features as well as a unique user identifier.
摘要翻译：推荐系统可以用于预测用户将相对于项目给出的用户行为。在一个实施例中，这样的预测用于使得可以向用户推荐项目。例如，产品可能会推荐给客户，潜在的朋友可能会推荐给社交网络工具的用户，组织可能会推荐给自动化用户或其他项目可能推荐给用户。在一个实施例中，存储器存储指定用户行为的双线性协同过滤模型的数据结构。在该实施例中，自动推理过程可以应用于数据结构，以便预测给定关于用户的信息的用户行为和关于项目的信息。例如，用户信息包括用户特征以及唯一的用户标识符。

4. 发明授权

US08433660B2 Managing a portfolio of experts 有权
标题翻译：管理专家组合
公开(公告)号：US08433660B2
公开(公告)日：2013-04-30
申请号：US12628421
申请日：2009-12-01
申请人： David Stern , Horst Cornelius Samulowitz , Ralf Herbrich , Thore Graepel
发明人： David Stern , Horst Cornelius Samulowitz , Ralf Herbrich , Thore Graepel
IPC分类号： G06F15/18 , G06F17/00 , G06F5/00 , G06F15/00 , G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G06N5/04 , G06Q10/00
摘要： Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction.
摘要翻译：描述专家组合的描述，专家可能是例如，自动化专家或人类专家。在一个实施例中，选择引擎从专家组合中选择专家，并将专家分配给指定的任务。例如，选择引擎具有贝叶斯机器学习系统，每当观察到任务上的专家表现时，该学习系统被迭代地更新。例如，将稀疏活动的二进制任务和专家特征向量输入到使用机器学习系统学习的映射将这些特征向量映射到多维特征空间的选择引擎。在示例中，映射向量的内积给出了对专家性能的概率分布的估计。在一个实施例中，专家是自动化问题解决者，并且任务是诸如约束满足问题或组合拍卖之类的硬组合问题。

5. 发明申请

US20120150771A1 Knowledge Corroboration 有权
标题翻译：知识佐证
公开(公告)号：US20120150771A1
公开(公告)日：2012-06-14
申请号：US12963352
申请日：2010-12-08
申请人： Gjergji Kasneci , Jurgen Ann Francois Marie Van Gael , Thore Graepel , Ralf Herbrich , David Stern
发明人： Gjergji Kasneci , Jurgen Ann Francois Marie Van Gael , Thore Graepel , Ralf Herbrich , David Stern
IPC分类号： G06F15/18
CPC分类号： G06N7/005
摘要： Knowledge corroboration is described. In an embodiment many judges provide answers to many questions so that at least one answer is provided to each question and at least some of the questions have answers from more than one judge. In an example a probabilistic learning system takes features describing the judges or the questions or both and uses those features to learn an expertise of each judge. For example, the probabilistic learning system has a graphical assessment component which aggregates the answers in a manner which takes into account the learnt expertise in order to determine enhanced answers. In an example the enhanced answers are used for knowledge base clean-up or web-page classification and the learnt expertise is used to select judges for future questions. In an example the probabilistic learning system has a logical component that propagates answers according to logical relations between the questions.
摘要翻译：描述知识佐证。在一个实施例中，许多法官为许多问题提供答案，使得至少一个答案被提供给每个问题，并且至少一些问题具有来自多于一个法官的答案。在一个例子中，概率学习系统采用描述法官或问题或两者的特征，并使用这些特征来学习每个法官的专业知识。例如，概率学习系统具有图形评估组件，其以考虑到所学习的专业知识的方式聚集答案，以便确定增强的答案。在一个例子中，增强的答案用于知识库清理或网页分类，并且学习的专业知识用于为将来的问题选择法官。在一个例子中，概率学习系统具有根据问题之间的逻辑关系传播答案的逻辑组件。

6. 发明申请

US20110131163A1 Managing a Portfolio of Experts 有权
标题翻译：管理专家组合
公开(公告)号：US20110131163A1
公开(公告)日：2011-06-02
申请号：US12628421
申请日：2009-12-01
申请人： David Stern , Horst Cornelius Samulowitz , Ralf Herbrich , Thore Graepel
发明人： David Stern , Horst Cornelius Samulowitz , Ralf Herbrich , Thore Graepel
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G06N5/04 , G06Q10/00
摘要： Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction.
摘要翻译：描述专家组合的描述，专家可能是例如，自动化专家或人类专家。在一个实施例中，选择引擎从专家组合中选择专家，并将专家分配给指定的任务。例如，选择引擎具有贝叶斯机器学习系统，每当观察到任务上的专家表现时，该学习系统被迭代地更新。例如，将稀疏活动的二进制任务和专家特征向量输入到使用机器学习系统学习的映射将这些特征向量映射到多维特征空间的选择引擎。在示例中，映射向量的内积给出了对专家性能的概率分布的估计。在一个实施例中，专家是自动化问题解决者，并且任务是诸如约束满足问题或组合拍卖之类的硬组合问题。

7. 发明申请

US20100100416A1 Recommender System 有权
标题翻译：推荐系统
公开(公告)号：US20100100416A1
公开(公告)日：2010-04-22
申请号：US12253854
申请日：2008-10-17
申请人： Ralf Herbrich , Thore Graepel , David Stern
发明人： Ralf Herbrich , Thore Graepel , David Stern
IPC分类号： G06F17/30
CPC分类号： G06Q30/0633 , G06Q30/02 , G06Q30/0203
摘要： A recommender system may be used to predict a user behavior that a user will give in relation to an item. In an embodiment such predictions are used to enable items to be recommended to users. For example, products may be recommended to customers, potential friends may be recommended to users of a social networking tool, organizations may be recommended to automated users or other items may be recommended to users. In an embodiment a memory stores a data structure specifying a bi-linear collaborative filtering model of user behaviors. In the embodiment an automated inference process may be applied to the data structure in order to predict a user behavior given information about a user and information about an item. For example, the user information comprises user features as well as a unique user identifier.
摘要翻译：推荐系统可以用于预测用户将相对于项目给出的用户行为。在一个实施例中，这样的预测用于使得可以向用户推荐项目。例如，产品可能会推荐给客户，潜在的朋友可能会推荐给社交网络工具的用户，组织可能会推荐给自动化用户或其他项目可能推荐给用户。在一个实施例中，存储器存储指定用户行为的双线性协同过滤模型的数据结构。在该实施例中，自动推理过程可以应用于数据结构，以便预测给定关于用户的信息的用户行为和关于项目的信息。例如，用户信息包括用户特征以及唯一的用户标识符。

8. 发明授权

US09413557B2 Pricing in social advertising 有权
标题翻译：社交广告定价
公开(公告)号：US09413557B2
公开(公告)日：2016-08-09
申请号：US12818161
申请日：2010-06-18
申请人： Ralf Herbrich , Thore Graepel , Yoram Bachrach
发明人： Ralf Herbrich , Thore Graepel , Yoram Bachrach
IPC分类号： G06Q30/00 , H04L12/58 , G06Q30/02 , G06Q50/00
CPC分类号： H04L12/5855 , G06Q30/0214 , G06Q30/0273 , G06Q50/01 , H04L51/14 , H04L51/32
摘要： Online recommendations are tracked through a forwarding service. The forwarding service can provide such statistics to an ad service, which can provide incentives to the recommending user and a consuming user. Example incentives may include an accumulation of points by the recommending user, a discount to the consuming user if a purchase is made in response to the recommendation, etc. To determine how much of an incentive each participant in the recommendation flow receives, a graph is created to model the recommendation flow and incentives are allocated using a cooperative game description based on this graph that associates each participant with a power index that represents that participants share of the incentive.
摘要翻译：通过转发服务跟踪在线建议。转发服务可以将这样的统计信息提供给广告服务，其可以向推荐用户和消费用户提供激励。示例性激励可以包括推荐用户的积分积分，如果响应于推荐而进行购买，则对消费用户的折扣等。为了确定推荐流程中每个参与者接收到的激励多少，创建以建模流程的模型和激励是使用基于该图的合作游戏描述来分配的，其将每个参与者与表示激励的参与者份额的权力指数相关联。

9. 发明申请

US20120089581A1 Informing Search Results Based on Commercial Transaction Publications 审中-公开
标题翻译：基于商业交易出版物的搜索结果通知
公开(公告)号：US20120089581A1
公开(公告)日：2012-04-12
申请号：US12899569
申请日：2010-10-07
申请人： Anoop Gupta , Thore Graepel , Ralf Herbrich
发明人： Anoop Gupta , Thore Graepel , Ralf Herbrich
IPC分类号： G06F17/30 , G06F15/16
CPC分类号： G06Q10/00 , G06Q30/00
摘要： A publishing engine captures capturing commercial events and other information (collectively, “commercial information”) associated with a first user and automatically notifies other users in the social network of the first user of this commercial information. The publishing engine also notifies one or more search engines of these events and information. Based on this commercial information, the search engine can augment search results of the members of the social network to include historical notifications relating to commercial transactions for similar products and/or services by others in their social network. In this manner, for example, the search engine can provide results directing the searcher to other users in their social network who have purchased such products and/or services.
摘要翻译：发布引擎捕获与第一用户相关联的商业事件和其他信息（统称为“商业信息”），并自动通知该商业信息的第一用户的社交网络中的其他用户。发布引擎还通知一个或多个搜索引擎的这些事件和信息。基于这种商业信息，搜索引擎可以增加社交网络成员的搜索结果，以包括与他们的社交网络中的其他类似产品和/或服务相关的商业交易的历史通知。以这种方式，例如，搜索引擎可以提供将搜索者指向已经购买了这样的产品和/或服务的社交网络中的其他用户的结果。

10. 发明申请

US20070112706A1 Handicapping in a Bayesian skill scoring framework 审中-公开
标题翻译：在贝叶斯技能评分框架中的障碍
公开(公告)号：US20070112706A1
公开(公告)日：2007-05-17
申请号：US11607482
申请日：2006-11-30
申请人： Ralf Herbrich , Thore Graepel
发明人： Ralf Herbrich , Thore Graepel
IPC分类号： G06F15/18
CPC分类号： G07F17/3274 , G07F17/32
摘要： A skill scoring frameworks allows for handicapping an individual game player in a gaming environment in preparation of matching the game player with other game players, whether for building teams or assigning competitors, or both. By introducing handicapping into the skill scoring framework, a highly skilled player may select one or more game characteristics (e.g., a less than optimal racing vehicle, reduced character capabilities, etc.) and therefore be assigned a handicap that allows the player to be matched with lower skilled players for competitive game play. Handicaps may apply positively or negatively a player's skill score during the matching stage. Handicaps may also be updated based on the game outcomes of the game play in which they were applied.
摘要翻译：技能评分框架允许在游戏环境中妨碍个人游戏玩家，以准备将游戏玩家与其他游戏玩家相匹配，无论是建立团队还是分配竞争对手，或两者兼有。通过在技能评分框架中引入障碍，高技能玩家可以选择一个或多个游戏特征（例如，不太优化的赛车，减少的角色能力等），并且因此被分配允许玩家匹配的障碍与较低技术的玩家竞争游戏。障碍可能在比赛阶段积极或消极地运用玩家的技能得分。还可以根据应用游戏结果的游戏结果更新障碍。

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