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1. 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。

2. 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。

3. 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。

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. 发明申请

US20130085852A1 DEAL AND AD TARGETING IN ASSOCIATION WITH EMAILS 审中-公开
标题翻译：与电子邮件联盟的交易和广告目标
公开(公告)号：US20130085852A1
公开(公告)日：2013-04-04
申请号：US13252558
申请日：2011-10-04
申请人： Jianchang Mao
发明人： Jianchang Mao
IPC分类号： G06Q30/02
CPC分类号： G06Q10/107 , G06Q30/0251
摘要： Techniques are provided which improve deal and advertisement targeting of users. Methods and systems may detect if an email contains deal information related to one or more deals. If an email contains deal information, the deal information may be extracted. If the user clicks on a link in the email, one or more additional deals which may be similar or related to the one or more deals received in the email may be selected based at least in part on the extracted deal information. The additional deals and/or advertisements related to the additional deals may be targeted to the user via email or via the user's browser application.
摘要翻译：提供了改进用户的交易和广告定位的技术。方法和系统可以检测电子邮件是否包含与一个或多个交易相关的交易信息。如果电子邮件包含交易信息，则可以提取交易信息。如果用户点击电子邮件中的链接，则可以至少部分地基于所提取的交易信息来选择可能与电子邮件中接收的一个或多个交易相似或相关的一个或多个附加交易。与额外交易有关的额外交易和/或广告可以通过电子邮件或通过用户的浏览器应用向用户定向。

8. 发明申请

US20130085845A1 FACILITATING DEAL COMPARISON AND ADVERTISING IN ASSOCIATION WITH EMAILS 审中-公开
标题翻译：促进与电子邮件联盟的交易比较和广告
公开(公告)号：US20130085845A1
公开(公告)日：2013-04-04
申请号：US13252471
申请日：2011-10-04
申请人： Jianchang Mao
发明人： Jianchang Mao
IPC分类号： G06Q30/02
CPC分类号： G06Q30/02 , G06Q10/107
摘要： Techniques are provided which improve deal and advertisement targeting of users, and which may include facilitating user comparison of deals. Methods and systems may detect if an email contains deal information related to one or more deals. If an email contains deal information, the deal information may be extracted. When the email is opened by the user, a link may be displayed on top of (e.g., overlaid on) the email. The link may be configured such that clicking on the link transmits a search query comprising the extracted deal information to a deal service. The deal service may retrieve one or more additional deals which may be similar or related to the one or more deals received in the email. The additional deals may be selected by the deal service based at least in part on the extracted deal information.
摘要翻译：提供了改进用户的交易和广告定位的技术，其可以包括促进用户对交易的比较。方法和系统可以检测电子邮件是否包含与一个或多个交易相关的交易信息。如果电子邮件包含交易信息，则可以提取交易信息。当用户打开电子邮件时，链接可能会显示在电子邮件的顶部（例如，覆盖）上。链接可以被配置为使得点击链接将包括提取的交易信息的搜索查询发送到交易服务。交易服务可以检索可能与电子邮件中接收的一个或多个交易相似或相关的一个或多个附加交易。交易服务可以至少部分地基于提取的交易信息来选择附加交易。

9. 发明授权

US07761436B2 Apparatus and method for controlling content access based on shared annotations for annotated users in a folksonomy scheme 有权
标题翻译：用于在民间学习方案中基于注释用户的共享注释来控制内容访问的装置和方法
公开(公告)号：US07761436B2
公开(公告)日：2010-07-20
申请号：US11325254
申请日：2006-01-03
申请人： Kenneth Norton , Chung-Man Tam , Jianchang Mao , Zhichen Xu , Adrienne Bassett , Ashley Hall , Nathan Arnold
发明人： Kenneth Norton , Chung-Man Tam , Jianchang Mao , Zhichen Xu , Adrienne Bassett , Ashley Hall , Nathan Arnold
IPC分类号： G06F7/00
CPC分类号： G06F17/30867 , G06F17/30997
摘要： A method for sharing content with a user includes receiving from a user a first set of keywords for annotating an annotated user; receiving from the user a second set of keywords that designate whether annotated content annotated by at least one keyword included in the second set of keywords may be shared with the annotated user; storing in a data store a first association of the first set of keywords with the annotated user, and a second association of the second set of keywords with the annotated user; receiving a keyword selection for a select keyword and an identifier for the annotated user; and displaying on the client system content annotated by the select keyword if the annotated user is annotated by at least one keyword in the first set of keywords, and if the select keyword is included in the second set of keywords.
摘要翻译：用于与用户共享内容的方法包括从用户接收用于注释注释用户的第一组关键字; 从所述用户接收第二组关键字，所述第二组关键字指定由所述第二组关键字中包括的至少一个关键字注释的注释内容是否可以与所注注的用户共享; 在数据存储器中存储第一关键字集合与注释用户的第一关联，以及第二关键字集合与注释用户的第二关联; 接收关键字选择用于选择关键字和标注用户的标识符; 以及如果所述注释用户在所述第一组关键字中由至少一个关键字注释，并且所述选择关键字是否包括在所述第二组关键字中，则在所述客户端系统上显示由所述选择关键字注释的内容。

10. 发明授权

US07031909B2 Method and system for naming a cluster of words and phrases 有权
公开(公告)号：US07031909B2
公开(公告)日：2006-04-18
申请号：US10096048
申请日：2002-03-12
申请人： Jianchang Mao , Sumit Taank , Christina Chung , Alpha Luk
发明人： Jianchang Mao , Sumit Taank , Christina Chung , Alpha Luk
IPC分类号： G06F17/20 , G06F17/28
CPC分类号： G06F17/2775 , G06F17/2715 , G06F17/2785 , G06F17/2795 , G06F17/30705 , Y10S707/99933
摘要： The present invention provides a method, system and computer program for naming a cluster, or a hierarchy of clusters, of words and phrases that have been extracted from a set of documents. The invention takes these clusters as the input and generates appropriate labels for the clusters using a lexical database. Naming involves first finding out all possible word senses for all the words in the cluster, using the lexical database; and then augmenting each word sense with words that are semantically similar to that word sense to form respective definition vectors. Thereafter, word sense disambiguation is done to find out the most relevant sense for each word. Definition vectors are clustered into groups. Each group represents a concept. These concepts are thereafter ranked based on their support. Finally, a pre-specified number of words and phrases from the definition vectors of the dominant concepts are selected as labels, based on their generality in the lexical database. Therefore, the labels may not necessarily consist of the original words in the cluster. A hierarchy of clusters is named in a recursive fashion starting from leaf clusters. Dominant concepts in child clusters are propagated into their parent to reduce the labeling complexity of parent clusters.

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