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81. 发明授权

US07788131B2 Advertising keyword cross-selling 有权
标题翻译：广告关键字交叉销售
公开(公告)号：US07788131B2
公开(公告)日：2010-08-31
申请号：US11300918
申请日：2005-12-15
申请人： Shuzhen Nong , Ying Li , Tarek Najm , Li Li , Hua-Jun Zeng , Zheng Chen , Benyu Zhang
发明人： Shuzhen Nong , Ying Li , Tarek Najm , Li Li , Hua-Jun Zeng , Zheng Chen , Benyu Zhang
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06F17/30864 , G06Q30/0251 , G06Q30/0275
摘要： Seed keywords are leveraged to provide expanded keywords that are then associated with relevant advertisers. Instances can also include locating potential advertisers based on the expanded keywords. Inverse lookup techniques are employed to determine which keywords are associated with an advertiser. Filtering can then be employed to eliminate inappropriate keywords for that advertiser. The keywords are then automatically revealed to the advertiser for consideration as relevant search terms for their advertisements. In this manner, revenue for a search engine and/or for an advertiser can be substantially enhanced through the automatic expansion of relevant search terms. Advertisers also benefit by having larger and more relevant search term selections automatically available to them, saving them both time and money.
摘要翻译：使用种子关键字来提供扩展的关键字，然后与相关的广告商相关联。实例还可以包括根据扩展的关键字定位潜在的广告客户。采用反向查找技术来确定哪些关键字与广告商相关联。然后可以使用过滤来消除该广告客户的不合适的关键字。然后，这些关键字会自动向广告客户显示，作为其广告的相关搜索字词。以这种方式，可以通过自动扩展相关搜索词来大大增强搜索引擎和/或广告商的收入。广告商也可以通过自动获得更大更多相关的搜索词选项来获益，从而节省时间和金钱。

82. 发明授权

US07707129B2 Text classification by weighted proximal support vector machine based on positive and negative sample sizes and weights 有权
标题翻译：基于正，负样本大小和权重的加权近端支持向量机进行文本分类
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。

83. 发明申请

US20070239638A1 Text classification by weighted proximal support vector machine 有权
标题翻译：通过加权近端支持向量机进行文本分类
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。

84. 发明申请

US20070239553A1 Collaborative filtering using cluster-based smoothing 审中-公开
标题翻译：使用基于群集的平滑的协同过滤
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。

85. 发明申请

US20060259473A1 System and method for utilizing the content of an online conversation to select advertising content and/or other relevant information for display 有权
标题翻译：用于利用在线对话的内容来选择广告内容和/或其他相关信息进行显示的系统和方法
公开(公告)号：US20060259473A1
公开(公告)日：2006-11-16
申请号：US11128788
申请日：2005-05-13
申请人： Ying Li , Li Li , Tarek Najm , Xianfang Wang , Hongbin Gao , Hua-Jun Zeng , Benyu Zhang , Zheng Chen
发明人： Ying Li , Li Li , Tarek Najm , Xianfang Wang , Hongbin Gao , Hua-Jun Zeng , Benyu Zhang , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , Y10S707/99937
摘要： Systems and methods for analyzing the content of online conversations using data mining technologies are provided. Methods and systems for utilizing the results of data mining technology implementation to retrieve relevant advertising content and/or other relevant information for display in association with the content of an online conversation are also provided.
摘要翻译：提供了使用数据挖掘技术分析在线对话内容的系统和方法。还提供了利用数据挖掘技术实现结果来检索相关广告内容和/或与在线对话内容相关联地显示的其他相关信息的方法和系统。

86. 发明授权

US08359276B2 Identifying influential persons in a social network 有权
标题翻译：识别社会网络中有影响力的人物
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。

87. 发明授权

US07594013B2 Creating home pages based on user-selected information of web pages 有权
标题翻译：根据用户选择的网页信息创建主页
公开(公告)号：US07594013B2
公开(公告)日：2009-09-22
申请号：US11136029
申请日：2005-05-24
申请人： Jian Wang , Hua-Jun Zeng , Chenxi Lin , Zheng Chen , Benyu Zhang , Bing Sun
发明人： Jian Wang , Hua-Jun Zeng , Chenxi Lin , Zheng Chen , Benyu Zhang , Bing Sun
IPC分类号： G06F15/173
CPC分类号： G06F17/3089
摘要： A method of creating a personal home page containing information of interest assembled from various web sites. The method includes the partitioning of web pages into web blocks. Users may collect various web blocks from different web pages and utilize those web blocks to define the dynamic personal homepage. In addition, the web blocks may be tracked to update content in the personal home page based on corresponding changes in the original web page.
摘要翻译：一种创建个人主页的方法，该个人主页包含从各种网站组装的感兴趣的信息。该方法包括将网页划分成网页块。用户可以从不同的网页收集各种网页块，并利用这些网页块定义动态个人主页。此外，可以基于原始网页中的相应变化来跟踪网页块以更新个人主页中的内容。

88. 发明申请

US20080126411A1 DEMOGRAPHIC PREDICTION USING A SOCIAL LINK NETWORK 审中-公开
标题翻译：使用社会链接网络进行人脸预测
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。

89. 发明申请

US20070239554A1 Cluster-based scalable collaborative filtering 有权
标题翻译：基于群集的可扩展协作过滤
公开(公告)号：US20070239554A1
公开(公告)日：2007-10-11
申请号：US11377480
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： G06F17/30867 , G06Q30/0212 , G06Q30/0601 , G06Q30/0631
摘要： Methods for determining a predictive rating are disclosed. In an embodiment, an active user is compared to a set of clusters. One or more of the clusters are determined to be most similar to the active user. From the one or more clusters, K users are determined to be most similar to the active user. Prior ratings for an item by the K users may be used to predict a rating for the item for the active user.
摘要翻译：公开了确定预测等级的方法。在一个实施例中，将活动用户与一组集群进行比较。集群中的一个或多个被确定为与活动用户最相似。从一个或多个集群中，K个用户被确定为与活动用户最相似。 K用户对某项目的先前评级可用于预测活动用户的项目评级。

90. 发明申请

US20070239431A1 Scalable probabilistic latent semantic analysis 有权
标题翻译：可扩展概率潜在语义分析
公开(公告)号：US20070239431A1
公开(公告)日：2007-10-11
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，特别是在提供大数据集和/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。

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