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1. 发明申请

US20100250523A1 SYSTEM AND METHOD FOR LEARNING A RANKING MODEL THAT OPTIMIZES A RANKING EVALUATION METRIC FOR RANKING SEARCH RESULTS OF A SEARCH QUERY 审中-公开
标题翻译：用于研究优化搜索查询的搜索结果的排名评估度量的排名模型的系统和方法
公开(公告)号：US20100250523A1
公开(公告)日：2010-09-30
申请号：US12415939
申请日：2009-03-31
申请人： Rong Jin , Jianchang Mao , Hamed Valizadegan , Ruofei Zhang
发明人： Rong Jin , Jianchang Mao , Hamed Valizadegan , Ruofei Zhang
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G06F16/951
摘要： An improved system and method for learning a ranking model that optimizes a ranking evaluation metric for ranking search results of a search query is provided. An optimized nDCG ranking model that optimizes an approximation of an average nDCG ranking evaluation metric may be generated from training data through an iterative boosting method for learning to more accurately rank a list of search results for a query. A combination of weak ranking classifiers may be iteratively learned that optimize an approximation of an average nDCG ranking evaluation metric for the training data by training a weak ranking classifier at each iteration for each document in the training data with a computed weight and assigned class label, and then updating the optimized nDCG ranking model by adding the weak ranking classifier with a combination weight to the optimized nDCG ranking model.
摘要翻译：提供了一种用于学习排名模型的改进的系统和方法，其优化用于对搜索查询的搜索结果进行排名的排名评估度量。可以通过迭代提升方法从训练数据生成优化平均nDCG排名评估度量的近似的优化的nDCG排名模型，用于学习以更精确地排列查询的搜索结果列表。可迭代地学习弱排序分类器的组合，通过用训练数据中的每个文档对训练数据中的每个文档训练弱排序分类器，利用计算的权重和分配的类标签来优化训练数据的平均nDCG排名评估度量的近似值，然后通过向优化的nDCG排名模型添加具有组合权重的弱排序分类器来更新优化的nDCG排名模型。

2. 发明申请

US20120084142A1 BID LANDSCAPE FORECASTING IN ONLINE ADVERTISING 审中-公开
标题翻译：预测在线广告的景观
公开(公告)号：US20120084142A1
公开(公告)日：2012-04-05
申请号：US12895773
申请日：2010-09-30
申请人： Wei Li , Ying Grace Cui , Ruofei Zhang , Jianchang Mao
发明人： Wei Li , Ying Grace Cui , Ruofei Zhang , Jianchang Mao
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0247 , G06Q30/02 , G06Q30/0202
摘要： Techniques are provided for advertiser bid forecasting in online advertising, including display advertising. Methods are provided in which key targeting-related user segments are determined from bidding statistics. A feature set is extracted from an impression opportunity, based at least in part on the bidding statistics. A gradient boosting descent tree technique is utilized in determining an initial bid forecasting result. A linear regression-based model is used in post-tuning to arrive at a post-tuned result. For short-term forecasting, this may be the final result. For long-term forecasting, a hybrid approach may be utilized with further processing including utilization of a publisher-specific model.
摘要翻译：提供技术用于在线广告中的广告客户出价预测，包括展示广告。提供了根据投标统计确定与关键目标相关的用户细分的方法。至少部分地基于出价统计信息，从展示机会中提取要素集。在确定初始出价预测结果时，采用梯度提升下降树技术。基于线性回归的模型用于后调整以得到后调整结果。对于短期预测，这可能是最终结果。对于长期预测，可以使用混合方法进一步处理，包括使用发布者特定模型。

3. 发明申请

US20110196739A1 SYSTEMS AND METHODS FOR EFFICIENTLY RANKING ADVERTISEMENTS BASED ON RELEVANCY AND CLICK FEEDBACK 审中-公开
标题翻译：基于相关性和点击反馈有效地排列广告的系统和方法
公开(公告)号：US20110196739A1
公开(公告)日：2011-08-11
申请号：US12701237
申请日：2010-02-05
申请人： Ruofei Zhang , Wei Li , Jianchang Mao
发明人： Ruofei Zhang , Wei Li , Jianchang Mao
IPC分类号： G06Q30/00 , G06F15/18
CPC分类号： G06Q30/02 , G06Q30/0254
摘要： The present invention provides a method and system for ranking and selecting advertisements based on relevancy, click feedback and click over expected click (COEC) data. Advertisements may be described as contextual, page-embedded advertisements appearing on publisher websites. The method and system includes storing page-advertisement relevancy features in a vector space model and historical impression and click features in a click feedback model and analyzing data in the vector space model and click feedback model. The method and system further includes storing empirical click-through data in a serving log and analyzing data therein. The method and system then generates a regression model based on the analyzed data, which is stored in a regression storage module. The method and system receives requests for advertisement content from client devices, selects a plurality of candidate advertisements based on the generated regression model and provides a plurality of advertisements to a client device.
摘要翻译：本发明提供了一种基于相关性，点击反馈和点击预期点击（COEC）数据来排序和选择广告的方法和系统。广告可能被描述为出现在发布商网站上的内容相关的页面嵌入式广告。该方法和系统包括在向量空间模型中存储页面广告相关性特征，并在点击反馈模型中记录历史印象和点击特征，并分析向量空间模型和点击反馈模型中的数据。所述方法和系统还包括将经验点击数据存储在服务日志中并在其中分析数据。然后，该方法和系统基于分析数据生成回归模型，该数据存储在回归存储模块中。该方法和系统从客户机接收对广告内容的请求，基于所生成的回归模型选择多个候选广告，并向客户端设备提供多个广告。

4. 发明申请

US20100070498A1 OPTIMIZATION FRAMEWORK FOR TUNING RANKING ENGINE 有权
标题翻译：调整排气机优化框架
公开(公告)号：US20100070498A1
公开(公告)日：2010-03-18
申请号：US12211307
申请日：2008-09-16
申请人： Ruofei Zhang , Jianchang Mao
发明人： Ruofei Zhang , Jianchang Mao
IPC分类号： G06F7/06 , G06F17/30 , G06F7/00
CPC分类号： G06F17/30864 , G06Q10/06 , G06Q30/02
摘要： Disclosed are apparatus and methods for facilitating the ranking of web objects. The method includes automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters. The adjusted weight values are provided to the ranking engine so as to generate a ranked set of web objects based on such adjusted weight values and their corresponding parameters, as well as a particular query. A relevance metric (e.g., that quantifies or qualifies how relevant the generated ranked set of web objects are for the particular query) is determined. The method includes automatically repeating the operations of adjusting the weight values, providing the adjusted weight values to the ranking engine, and determining a relevance metric until the relevance metric reaches an optimized level, which corresponds to an optimized set of weight values. The repeated operations utilize one or more sets of weight values including at least one set that results in a worst relevance metric value, as compared to a previous set of weight values, according to a certain probability in order to escape local optimal solution to reach the global optimal solution.
摘要翻译：公开了用于促进web对象的排名的装置和方法。该方法包括自动调整用于多个参数的多个权重值，用于输入适应于基于这些权重值及其对应参数对多个网页对象排序的排名引擎。调整的权重值被提供给排名引擎，以便基于这种调整的权重值及其对应的参数以及特定的查询来生成排序的web对象集合。确定相关性度量（例如，量化或限定生成的排名的web对象集合对于特定查询的相关性）。该方法包括自动重复调整权重值的操作，向排序引擎提供经调整的权重值，以及确定相关性度量，直到相关性度量达到对应于优化的权重值集合的优化级别。重复操作利用一组或多组权重值，包括至少一组，与根据某种概率的先前的权重值组相比导致最差的相关度度值，以逃避局部最优解以达到全局最优解。

5. 发明申请

US20090198671A1 SYSTEM AND METHOD FOR GENERATING SUBPHRASE QUERIES 审中-公开
标题翻译：用于生成SUBPHRASE QUERIES的系统和方法
公开(公告)号：US20090198671A1
公开(公告)日：2009-08-06
申请号：US12025947
申请日：2008-02-05
申请人： Ruofei Zhang , Haibin Cheng , Yefei Peng , Benjamin Rey , Jianchang Mao
发明人： Ruofei Zhang , Haibin Cheng , Yefei Peng , Benjamin Rey , Jianchang Mao
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , G06F16/3338
摘要： A system for generating subphrase queries. The system includes a sequence label modeling engine and a regression modeling engine. The sequence label modeling engine generates a plurality of subphrase queries by indexing through each token in a search phrase and labeling each token based on an association to other tokens in the search phrase. The regression modeling engine scores each subphrase query at least partially on the association according to a scoring model. The regression modeling engine identifies the subphrase query with the highest score which may then be used for identifying a sponsored search list or a web search item.
摘要翻译：用于生成子字符查询的系统。该系统包括序列标签建模引擎和回归建模引擎。序列标签建模引擎通过在搜索短语中通过每个令牌进行索引来生成多个子句查询，并且基于与搜索短语中的其他令牌的关联来标记每个令牌。回归建模引擎至少部分地根据评分模型对关联进行评分。回归建模引擎识别具有最高分数的子句子查询，然后可以用于标识赞助的搜索列表或网络搜索项目。

6. 发明授权

US08108374B2 Optimization framework for tuning ranking engine 有权
标题翻译：调整排名引擎的优化框架
公开(公告)号：US08108374B2
公开(公告)日：2012-01-31
申请号：US12211307
申请日：2008-09-16
申请人： Ruofei Zhang , Jianchang Mao
发明人： Ruofei Zhang , Jianchang Mao
IPC分类号： G06F17/30
CPC分类号： G06F17/30864 , G06Q10/06 , G06Q30/02
摘要： Disclosed are apparatus and methods for facilitating the ranking of web objects. The method includes automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters. The adjusted weight values are provided to the ranking engine so as to generate a ranked set of web objects based on such adjusted weight values and their corresponding parameters, as well as a particular query. A relevance metric (e.g., that quantifies or qualifies how relevant the generated ranked set of web objects are for the particular query) is determined. The method includes automatically repeating the operations of adjusting the weight values, providing the adjusted weight values to the ranking engine, and determining a relevance metric until the relevance metric reaches an optimized level, which corresponds to an optimized set of weight values. The repeated operations utilize one or more sets of weight values including at least one set that results in a worst relevance metric value, as compared to a previous set of weight values, according to a certain probability in order to escape local optimal solution to reach the global optimal solution.
摘要翻译：公开了用于促进web对象的排名的装置和方法。该方法包括自动调整用于多个参数的多个权重值，用于输入适应于基于这些权重值及其对应参数对多个网页对象排序的排名引擎。调整的权重值被提供给排名引擎，以便基于这种调整的权重值及其对应的参数以及特定的查询来生成排序的web对象集合。确定相关性度量（例如，量化或限定生成的排名的web对象集合对于特定查询的相关性）。该方法包括自动重复调整权重值的操作，向排序引擎提供经调整的权重值，以及确定相关性度量，直到相关性度量达到对应于优化的权重值集合的优化级别。重复操作利用一组或多组权重值，包括至少一组，与根据某种概率的先前的权重值组相比导致最差的相关度度值，以逃避局部最优解以达到全局最优解。

7. 发明授权

US07814040B1 System and method for image annotation and multi-modal image retrieval using probabilistic semantic models 有权
标题翻译：使用概率语义模型的图像注释和多模态图像检索的系统和方法
公开(公告)号：US07814040B1
公开(公告)日：2010-10-12
申请号：US11626835
申请日：2007-01-24
申请人： Ruofei Zhang , Zhongfei Zhang
发明人： Ruofei Zhang , Zhongfei Zhang
IPC分类号： G06F17/00
CPC分类号： G06F17/30256 , G06F17/30253 , G06K9/6278
摘要： Systems and Methods for multi-modal or multimedia image retrieval are provided. Automatic image annotation is achieved based on a probabilistic semantic model in which visual features and textual words are connected via a hidden layer comprising the semantic concepts to be discovered, to explicitly exploit the synergy between the two modalities. The association of visual features and textual words is determined in a Bayesian framework to provide confidence of the association. A hidden concept layer which connects the visual feature(s) and the words is discovered by fitting a generative model to the training image and annotation words. An Expectation-Maximization (EM) based iterative learning procedure determines the conditional probabilities of the visual features and the textual words given a hidden concept class. Based on the discovered hidden concept layer and the corresponding conditional probabilities, the image annotation and the text-to-image retrieval are performed using the Bayesian framework.
摘要翻译：提供了多模式或多媒体图像检索的系统和方法。基于概率语义模型实现自动图像注释，其中通过包含要发现的语义概念的隐藏层连接视觉特征和文本词，以明确地利用两种模态之间的协同作用。视觉特征和文本词的关联在贝叶斯框架中确定，以提供关联的信心。通过将生成模型拟合到训练图像和注释词中，发现连接视觉特征和单词的隐藏概念层。基于期望最大化（EM）的迭代学习过程确定视觉特征的条件概率和给定隐藏概念类的文本词。基于发现的隐藏概念层和相应的条件概率，使用贝叶斯框架来执行图像注释和文本到图像检索。

8. 发明申请

US20080060013A1 Video channel creation systems and methods 有权
标题翻译：视频频道创建系统和方法
公开(公告)号：US20080060013A1
公开(公告)日：2008-03-06
申请号：US11517495
申请日：2006-09-06
申请人： Ramesh R. Sarukkai , John Thrall , Ruofei Zhang , Sai Surya Kiran Evani
发明人： Ramesh R. Sarukkai , John Thrall , Ruofei Zhang , Sai Surya Kiran Evani
IPC分类号： G06F13/00 , G06F3/00 , H04N5/445
CPC分类号： G11B27/034 , G06F17/30781 , H04N21/278 , H04N21/4828
摘要： The present invention is directed towards systems and methods for generating one or more channels for the organization of content items. A method according to one embodiment comprises selecting a content item and one or more items of metadata for the selected content item. A determination is made to determine if the selected content item should be associated with a given channel on the basis of the metadata. A channel is generated on the basis of the determination, with the selected content item organized in association with the channel.
摘要翻译：本发明涉及用于生成用于组织内容项的一个或多个信道的系统和方法。根据一个实施例的方法包括为所选择的内容项目选择内容项目和一个或多个元数据项目。确定所选择的内容项目是否应该基于元数据与给定的频道相关联。基于确定生成频道，其中所选择的内容项目与频道相关联地组织。

9. 发明申请

US20130339126A1 CAMPAIGN PERFORMANCE FORECASTING FOR NON-GUARANTEED DELIVERY ADVERTISING 审中-公开
标题翻译：对非担保交货广告的考核绩效预测
公开(公告)号：US20130339126A1
公开(公告)日：2013-12-19
申请号：US13495614
申请日：2012-06-13
申请人： Ying Grace Cui , Ruofei Zhang
发明人： Ying Grace Cui , Ruofei Zhang
IPC分类号： G06Q30/02
CPC分类号： G06Q30/02
摘要： Advertisement (“ad”) campaign forecasting may predict results for the campaign and the ads included in the campaign. The results may include a forecast for impressions, clicks, conversions, and/or interactions with the ads. The forecasting may be utilized for display advertising with non-guaranteed delivery (“NGD”) systems in which a bidding platform allows advertisers to bid for ad impressions, clicks, and/or conversions. Forecasting results may be used by advertisers to manage and optimize campaigns.
摘要翻译：广告（“广告”）广告系列预测可能会预测广告系列和广告系列中包含的广告的结果。结果可能包括对广告的展示次数，点击次数，转化次数和/或互动次数的预测。该预测可用于具有非保证交付（“NGD”）系统的展示广告，其中出价平台允许广告商对广告展示，点击和/或转换进行出价。广告客户可能会使用预测结果来管理和优化广告系列。

10. 发明授权

US07826657B2 Automatically generating a content-based quality metric for digital images 有权
标题翻译：自动生成基于内容的数字图像质量指标
公开(公告)号：US07826657B2
公开(公告)日：2010-11-02
申请号：US11637422
申请日：2006-12-11
申请人： Ruofei Zhang , Ramesh R. Sarukkai , Subodh Shakya
发明人： Ruofei Zhang , Ramesh R. Sarukkai , Subodh Shakya
IPC分类号： G06K9/00
CPC分类号： G06K9/00711 , G06K9/4652
摘要： Techniques are described herein for automatically evaluating the quality of digital images based on one or more color characteristics of the images. In some embodiments, a quality metric that indicates the likelihood that the digital images convey semantics is generated based on color characteristics of the digital images. The quality metric may be used, for example, to determine which keyframe to use to make a thumbnail to represent video data. In some embodiments, feature values are generated for an image based on color characteristics of the image, and the feature values are assigned to bins. In such embodiments, the quality metric may be generated to indicate how uniform the distribution of feature values is among the bins.
摘要翻译：本文描述了基于图像的一个或多个颜色特性来自动评估数字图像的质量的技术。在一些实施例中，基于数字图像的颜色特性来生成指示数字图像传送语义的可能性的质量度量。例如，可以使用质量度量来确定用于制作缩略图以表示视频数据的关键帧。在一些实施例中，基于图像的颜色特征为图像生成特征值，并且将特征值分配给箱。在这样的实施例中，可以生成质量度量以指示特征值的分布在箱之间的均匀度。

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