公开(公告)号：US20080070209A1

公开(公告)日：2008-03-20

申请号：US11533742

申请日：2006-09-20

申请人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

发明人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

IPC分类号： G09B19/00

CPC分类号： G06Q30/02 ,  G06Q10/10

摘要： An influential persons identification system and method for identifying a set of influential persons (or influencers) in a social network (such as an online social network). The influential persons set is generated such that by sending a message to the set the message will be propagated through the network at the greatest speed and coverage. A ranking of users is generated, and a pruning process is performed starting with the top-ranked user and working down the list. For each user on the list, the user is identified as an influencer and then the user and each of his friends are deleted from the social network users list. Next, the same process is performed for the second-ranked user, the third-ranked user, and so forth. The process terminates when the list of users of the social network is exhausted or the desired number of influencers on the influential person set is reached.

摘要翻译： 在社交网络（如在线社交网络）中识别一组有影响力的人（或影响者）的有影响力的人员识别系统和方法。 产生有影响力的人员，通过发送消息给消息集，消息将以最大的速度和覆盖率通过网络传播。 生成用户排名，并从顶级用户开始执行修剪过程，并在列表中执行操作。 对于列表中的每个用户，用户被识别为影响者，然后从社交网络用户列表中删除用户和他的每个朋友。 接下来，对于第二等级的用户，第三等级的用户等执行相同的处理。 当社交网络的用户列表用完或者达到期望数量的有影响力的人集合的影响者时，该过程终止。

公开(公告)号：US08359276B2

公开(公告)日：2013-01-22

申请号：US11533742

申请日：2006-09-20

申请人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

发明人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

IPC分类号： G06Q99/00

CPC分类号： G06Q30/02 ,  G06Q10/10

摘要： An influential persons identification system and method for identifying a set of influential persons (or influencers) in a social network (such as an online social network). The influential persons set is generated such that by sending a message to the set the message will be propagated through the network at the greatest speed and coverage. A ranking of users is generated, and a pruning process is performed starting with the top-ranked user and working down the list. For each user on the list, the user is identified as an influencer and then the user and each of his friends are deleted from the social network users list. Next, the same process is performed for the second-ranked user, the third-ranked user, and so forth. The process terminates when the list of users of the social network is exhausted or the desired number of influencers on the influential person set is reached.

摘要翻译： 在社交网络（如在线社交网络）中识别一组有影响力的人（或影响者）的有影响力的人员识别系统和方法。 产生有影响力的人员，通过发送消息给消息集，消息将以最大的速度和覆盖率通过网络传播。 生成用户排名，并从顶级用户开始执行修剪过程，并在列表中执行操作。 对于列表中的每个用户，用户被识别为影响者，然后从社交网络用户列表中删除用户和他的每个朋友。 接下来，对于第二等级的用户，第三等级的用户等执行相同的处理。 当社交网络的用户列表用完或者达到期望数量的有影响力的人集合的影响者时，该过程终止。

公开(公告)号：US20080126411A1

公开(公告)日：2008-05-29

申请号：US11535160

申请日：2006-09-26

申请人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa B. Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

发明人： Dong Zhuang ,  Benyu Zhang ,  Heng Zhang ,  Jeremy Tantrum ,  Teresa B. Mah ,  Hua-Jun Zeng ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F7/00

CPC分类号： G06Q30/02 ,  G06Q10/04

摘要： A system, method, computer-readable media, and related techniques are disclosed for predicting demographic information of a user. A social link network is created and a search request for demographic information related to a first user within the social link network is received. The requested demographic information based on the demographic information of other users connected to the first user within the social link network is provided.

摘要翻译： 公开了一种用于预测用户的人口统计信息的系统，方法，计算机可读介质和相关技术。 创建社交链接网络，并且接收与社交链路网络内的第一用户相关的针对人口统计信息的搜索请求。 提供了基于在社交网络中连接到第一用户的其他用户的人口统计信息的请求的人口统计信息。

公开(公告)号：US07707129B2

公开(公告)日：2010-04-27

申请号：US11384889

申请日：2006-03-20

申请人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

发明人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

IPC分类号： G06F15/18 ,  G06E1/00 ,  G06E3/00

CPC分类号： G06F17/30707 ,  G06K9/6269

摘要： Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.

摘要翻译： 本发明的实施例涉及对支持向量机（SVM）分类模型的改进。 当文本数据显着不平衡（即正负标签数据不成比例）时，标准SVM的分类质量恶化。 本发明的实施例涉及一种加权近端SVM（WPSVM）模型，其实现与传统SVM模型基本相同的精度，同时需要显着更少的计算时间。 根据本发明的实施例的加权近端SVM（WPSVM）模型可以包括每个训练误差的权重和用于估计权重的方法，其自动地解决不平衡数据问题。 而且，不是通过KKT（Karush-Kuhn-Tucker）条件和Sherman-Morrison-Woodbury公式来解决优化问题，而是本发明的实施例使用迭代算法来解决无约束优化问题，这使得WPSVM适合于相对分类 高维数据。

公开(公告)号：US20070239638A1

公开(公告)日：2007-10-11

申请号：US11384889

申请日：2006-03-20

申请人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

发明人： Dong Zhuang ,  Benyu Zhang ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

IPC分类号： G06F15/18

CPC分类号： G06F17/30707 ,  G06K9/6269

摘要： Embodiments of the invention relate to improvements to the support vector machine (SVM) classification model. When text data is significantly unbalanced (i.e., positive and negative labeled data are in disproportion), the classification quality of standard SVM deteriorates. Embodiments of the invention are directed to a weighted proximal SVM (WPSVM) model that achieves substantially the same accuracy as the traditional SVM model while requiring significantly less computational time. A weighted proximal SVM (WPSVM) model in accordance with embodiments of the invention may include a weight for each training error and a method for estimating the weights, which automatically solves the unbalanced data problem. And, instead of solving the optimization problem via the KKT (Karush-Kuhn-Tucker) conditions and the Sherman-Morrison-Woodbury formula, embodiments of the invention use an iterative algorithm to solve an unconstrained optimization problem, which makes WPSVM suitable for classifying relatively high dimensional data.

摘要翻译： 本发明的实施例涉及对支持向量机（SVM）分类模型的改进。 当文本数据显着不平衡（即正负标签数据不成比例）时，标准SVM的分类质量恶化。 本发明的实施例涉及一种加权近端SVM（WPSVM）模型，其实现与传统SVM模型基本相同的精度，同时需要显着更少的计算时间。 根据本发明的实施例的加权近端SVM（WPSVM）模型可以包括每个训练误差的权重以及用于估计权重的方法，其自动地解决不平衡数据问题。 而且，不是通过KKT（Karush-Kuhn-Tucker）条件和Sherman-Morrison-Woodbury公式来解决优化问题，而是本发明的实施例使用迭代算法来解决无约束优化问题，这使得WPSVM适合于相对分类 高维数据。

公开(公告)号：US20070208728A1

公开(公告)日：2007-09-06

申请号：US11366526

申请日：2006-03-03

申请人： Benyu Zhang ,  Honghua Dai ,  Hua-Jun Zeng ,  Li Qi ,  Tarek Najm ,  Teresa Mah ,  Vladimir Shipunov ,  Ying Li ,  Zheng Chen

发明人： Benyu Zhang ,  Honghua Dai ,  Hua-Jun Zeng ,  Li Qi ,  Tarek Najm ,  Teresa Mah ,  Vladimir Shipunov ,  Ying Li ,  Zheng Chen

IPC分类号： G06F17/30

CPC分类号： G06F16/337 ,  G06F16/951

摘要： This invention provides a system and method for predicting user demographic attributes for non-registered users and users with incomplete profiles. The invention uses demographic information from registered users and user search history logs to create a database of information that associates the users' search history habits with their demographic attributes. The invention creates a first database that associates users' search query history with their demographic attributes, and also creates a second database that associates web pages that users have visited frequently along with the users' demographic attributes. The invention can compare the searching and browsing habits of non-registered users and users with incomplete profiles to the searching and browsing habits of registered users. Through the comparison, the invention can use the corresponding demographic attributes of the registered users to predict the demographic attributes of the non-registered users and the registered users with incomplete profiles.

摘要翻译： 本发明提供一种用于预测非注册用户和具有不完整简档的用户的用户人口统计属性的系统和方法。 本发明使用来自注册用户和用户搜索历史日志的人口统计信息来创建将用户的搜索历史习惯与其人口统计属性相关联的信息数据库。 本发明创建了将用户的搜索查询历史与其人口统计属性相关联的第一数据库，并且还创建了将用户经常访问的网页与用户的人口统计属性一起关联的第二数据库。 本发明可以将注册用户和注册用户不完整的用户的搜索和浏览习惯与注册用户的搜索和浏览习惯进行比较。 通过比较，本发明可以使用注册用户的相应人口统计特性来预测非注册用户和具有不完整简档的注册用户的人口统计属性。

公开(公告)号：US07844449B2

公开(公告)日：2010-11-30

申请号：US11392763

申请日：2006-03-30

申请人： Chenxi Lin ,  Jie Han ,  Guirong Xue ,  Hua-Jun Zeng ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

发明人： Chenxi Lin ,  Jie Han ,  Guirong Xue ,  Hua-Jun Zeng ,  Benyu Zhang ,  Zheng Chen ,  Jian Wang

IPC分类号： G06F17/27

CPC分类号： G06F17/2785

摘要： A scalable two-pass scalable probabilistic latent semantic analysis (PLSA) methodology is disclosed that may perform more efficiently, and in some cases more accurately, than traditional PLSA, especially where large and/or sparse data sets are provided for analysis. The improved methodology can greatly reduce the storage and/or computational costs of training a PLSA model. In the first pass of the two-pass methodology, objects are clustered into groups, and PLSA is performed on the groups instead of the original individual objects. In the second pass, the conditional probability of a latent class, given an object, is obtained. This may be done by extending the training results of the first pass. During the second pass, the most likely latent classes for each object are identified.

摘要翻译： 公开了一种可扩展的双向可伸缩概率潜在语义分析（PLSA）方法，其可以比传统的PLSA更有效地执行，在某些情况下可以更准确地执行，特别是在提供大型和/或稀疏数据集用于分析的情况下。 改进的方法可以大大降低培训PLSA模型的存储和/或计算成本。 在双路方法的第一遍中，对象被聚集成组，并且PLSA在组而不是原始的单个对象上执行。 在第二遍中，获得给定对象的潜在类的条件概率。 这可以通过扩展第一遍的训练结果来完成。 在第二遍期间，识别每个对象最可能的潜在类。

公开(公告)号：US20070219945A1

公开(公告)日：2007-09-20

申请号：US11372365

申请日：2006-03-09

申请人： Min Wang ,  Benyu Zhang ,  Hua-Jun Zeng ,  Jian Wang ,  Shiguang Liu ,  Zheng Chen

发明人： Min Wang ,  Benyu Zhang ,  Hua-Jun Zeng ,  Jian Wang ,  Shiguang Liu ,  Zheng Chen

IPC分类号： G06F17/30 ,  G06F3/048

CPC分类号： G06F17/30713 ,  G06F17/272 ,  G06F17/2775 ,  G06F17/30643 ,  Y10S707/92

摘要： Computer-readable media having computer-executable instructions and apparatuses provide a keyphrase navigation map (KNM) for a document page. Keyphrases are extracted from the document page. Keyphrase clusters are subsequently formed by a measure of relevancy, and a salient keyphrase is determined for each cluster. A thumbnail is formed with tags corresponding to the salient keyphrases. A selected tag is expanded with associated keyphrases. An associated keyphrase may be further selected in order to facilitate the navigation of the document page. The displayed tags on the thumbnail are positioned in accordance with locations of associated keyphrases in the document page.

摘要翻译： 具有计算机可执行指令和装置的计算机可读介质为文档页面提供关键词导航映射（KNM）。 从文档页面提取关键短语。 随后通过相关性的量度形成关键词组，并且为每个簇确定显着的关键短语。 使用与突出关键短语相对应的标签形成缩略图。 所选标签用相关的关键短语展开。 可以进一步选择相关联的关键短语，以便于文档页面的导航。 缩略图上显示的标签根据文档页面中相关联的关键短语的位置进行定位。

公开(公告)号：US20070239553A1

公开(公告)日：2007-10-11

申请号：US11377130

申请日：2006-03-16

申请人： Chenxi Lin ,  Gui-Rong Xue ,  Hua-Jun Zeng ,  Zheng Chen ,  Benyu Zhang ,  Jian Wang

发明人： Chenxi Lin ,  Gui-Rong Xue ,  Hua-Jun Zeng ,  Zheng Chen ,  Benyu Zhang ,  Jian Wang

IPC分类号： G06Q30/00

CPC分类号： G06Q30/0623 ,  G06F16/9535 ,  G06Q30/0242 ,  G06Q30/0274

摘要： In an embodiment, a method of predicting an active user's rating for an item is disclosed. A database of users may be sorted into clusters. The data associated with the users in each cluster may be smoothed to filling in ratings for items that the users have not personally rated. An active user may then be compared to a set of users, where the set may be all or some portion of the database, to determine the K users that are most similar to the active user. The ratings of the K users regarding the item may be used to predict the active user's rating for the item. In an embodiment, the rating of each of the K users is assigned a confidence value associated with whether the user personally rated the item or if the rating was generated by the data smoothing process.

摘要翻译： 在一个实施例中，公开了一种用于预测项目的活跃用户评级的方法。 可以将用户的数据库分类为群集。 可以平滑与每个群集中的用户相关联的数据，以填充用户未被评估的项目的评级。 然后可以将活动用户与一组用户进行比较，其中该集合可以是数据库的全部或部分，以确定与活动用户最相似的K个用户。 关于该项目的K个用户的评级可以用于预测该项目的活动用户的评级。 在一个实施例中，每个K个用户的评级被分配与用户个人评价该项目相关联的置信度值，或者如果该评级是由数据平滑处理产生的。

公开(公告)号：US07529719B2

公开(公告)日：2009-05-05

申请号：US11378095

申请日：2006-03-17

申请人： Ning Liu ,  Benyu Zhang ,  Jun Yan ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

发明人： Ning Liu ,  Benyu Zhang ,  Jun Yan ,  Zheng Chen ,  Hua-Jun Zeng ,  Jian Wang

IPC分类号： G06N5/00

CPC分类号： G06N5/02 ,  G06F17/30705

摘要： Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

摘要翻译： 具有计算机可执行指令和设备的计算机可读介质将文档或语料库分类。 张量空间模型（TSM），其通过高阶张量对文本进行建模，表示文档或文档语料库。 由多线代数技术支持，TSM为多因素结构分析提供了框架。 TSM还受到操作和提出的工具的支持，例如用于降低高阶张量尺寸的高阶奇异值分解（HOSVD）。 将尺寸减小的张量与表示可能类别的张量进行比较。 因此，文档或文档的语料库选择一个类别。 20个新闻组的数据集的实验结果表明，TSM对于文本分类的向量空间模型（VSM）是有利的。