公开(公告)号：US20110029466A1

公开(公告)日：2011-02-03

申请号：US12906010

申请日：2010-10-15

申请人： Tie-Yan Liu ,  Hang Li ,  Yu-Ting Liu

发明人： Tie-Yan Liu ,  Hang Li ,  Yu-Ting Liu

IPC分类号： G06F15/18 ,  G06F7/38

CPC分类号： G06F17/3053 ,  G06N7/005 ,  Y10S707/99931 ,  Y10S707/99933

摘要： A method and system for rank aggregation of entities based on supervised learning is provided. A rank aggregation system provides an order-based aggregation of rankings of entities by learning weights within an optimization framework for combining the rankings of the entities using labeled training data and the ordering of the individual rankings. The rank aggregation system is provided with multiple rankings of entities. The rank aggregation system is also provided with training data that indicates the relative ranking of pairs of entities. The rank aggregation system then learns weights for each of the ranking sources by attempting to optimize the difference between the relative rankings of pairs of entities using the weights and the relative rankings of pairs of entities of the training data.

摘要翻译： 提供了一种基于监督学习的实体等级聚合的方法和系统。 排名聚合系统通过在优化框架内学习权重来提供实体排序的基于订单的聚合，以使用标记的训练数据和个体排名的顺序组合实体的排名。 排名聚合系统提供多个实体排名。 等级聚合系统还提供了指示实体对的相对排名的训练数据。 秩聚合系统然后通过尝试使用训练数据的实体对的权重和相对排名来优化实体对的相对排名之间的差异来学习每个排名来源的权重。

公开(公告)号：US20100082617A1

公开(公告)日：2010-04-01

申请号：US12237296

申请日：2008-09-24

申请人： Tie-Yan Liu ,  Hang Li

发明人： Tie-Yan Liu ,  Hang Li

IPC分类号： G06F17/30

CPC分类号： G06F16/334

摘要： The present invention provides techniques for generating data that is used for ranking documents. In one embodiment, a method involves the step of extracting data features from a number of documents to be ranked. The data features extracted from the documents are established in conjunction with a first feature map and a second feature map, wherein the first feature map and the second feature map are capable of keeping the relative ordering between two document instances. In one embodiment, the two feature maps are specially a divide feature map and a minus feature map. Once the data is mapped, the method involves the step of generating pairwise preferences from the first feature map and the second feature map. Then the pairwise preferences are aggregated into a total order, which can be used to produce one or more relevancy scores.

摘要翻译： 本发明提供了用于生成用于对文档进行排序的数据的技术。 在一个实施例中，一种方法包括从要排名的多个文档中提取数据特征的步骤。 结合第一特征图和第二特征图建立从文档提取的数据特征，其中第一特征图和第二特征图能够保持两个文档实例之间的相对排序。 在一个实施例中，两个特征图特别是分割特征图和负特征图。 一旦数据被映射，该方法涉及从第一特征图和第二特征图生成成对偏好的步骤。 然后，成对偏好被聚合成总顺序，其可用于产生一个或多个相关性分数。

公开(公告)号：US20090187555A1

公开(公告)日：2009-07-23

申请号：US12017288

申请日：2008-01-21

申请人： Tie-Yan Liu ,  Geng Xiubo ,  Tao Qin ,  Hang Li

发明人： Tie-Yan Liu ,  Geng Xiubo ,  Tao Qin ,  Hang Li

IPC分类号： G06F17/30

CPC分类号： G06F17/30675

摘要： This disclosure describes various exemplary methods, computer program products, and systems for selecting features for ranking in information retrieval. This disclosure describes calculating importance scores for features, measuring similarity scores between two features, selecting features that maximizes total importance scores of the features and minimizes total similarity scores between the features. Also, the disclosure includes selecting features for ranking that solves an optimization problem. Thus, this disclosure identifies relevant features by removing noisy and redundant features and speeds up a process of model training.

摘要翻译： 本公开描述了各种示例性方法，计算机程序产品和用于选择用于在信息检索中排名的特征的系统。 该公开内容描述了计算特征的重要度得分，测量两个特征之间的相似性得分，选择使特征的总重要度得分最大化的特征并使特征之间的总相似性得分最小化的特征。 此外，本公开包括选择解决优化问题的排名特征。 因此，本公开通过去除噪声和冗余特征并加速模型训练的过程来识别相关特征。

公开(公告)号：US20090106222A1

公开(公告)日：2009-04-23

申请号：US11874813

申请日：2007-10-18

申请人： Tie-Yan Liu ,  Hang Li ,  Tao Qin ,  Zhe Cao

发明人： Tie-Yan Liu ,  Hang Li ,  Tao Qin ,  Zhe Cao

IPC分类号： G06F7/00

CPC分类号： G06F17/30864

摘要： Procedures for learning and ranking items in a listwise manner are discussed. A listwise methodology may consider a ranked list, of individual items, as a specific permutation of the items being ranked. In implementations, a listwise loss function may be used in ranking items. A listwise loss function may be a metric which reflects the departure or disorder from an exemplary ranking for one or more sample listwise rankings used in learning. In this manner, the loss function may approximate the exemplary ranking for the plurality of items being ranked.

摘要翻译： 讨论了以列表方式学习和排序项目的程序。 列表方法可以将个别项目的排名列表视为被排序的项目的具体置换。 在实现中，可以在排序项中使用列表丢失函数。 列表损失函数可以是反映学习中使用的一个或多个样本列表排序的示例性排名的偏离或混乱的度量。 以这种方式，损失函数可以近似排列的多个项目的示例性排名。

公开(公告)号：US20080027925A1

公开(公告)日：2008-01-31

申请号：US11460838

申请日：2006-07-28

申请人： Hang Li ,  Jun Xu ,  Yunbo Cao ,  Tie-Yan Liu

发明人： Hang Li ,  Jun Xu ,  Yunbo Cao ,  Tie-Yan Liu

IPC分类号： G06F17/30

CPC分类号： G06F17/30864 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99938 ,  Y10S707/99939

摘要： A method and system for generating a ranking function to rank the relevance of documents to a query is provided. The ranking system learns a ranking function from training data that includes queries, resultant documents, and relevance of each document to its query. The ranking system learns a ranking function using the training data by weighting incorrect rankings of relevant documents more heavily than the incorrect rankings of not relevant documents so that more emphasis is placed on correctly ranking relevant documents. The ranking system may also learn a ranking function using the training data by normalizing the contribution of each query to the ranking function so that it is independent of the number of relevant documents of each query.

摘要翻译： 提供了一种用于生成用于将文档与查询的相关性排序的排序函数的方法和系统。 排名系统从包括查询，结果文档以及每个文档与其查询的相关性的训练数据中学习排名函数。 排名系统使用训练数据通过对相关文件的不正确排名加权比不相关文件的不正确排名更多地学习排名功能，以便更加重视正确排列相关文件。 排序系统还可以通过将每个查询的贡献归一化到排序函数来学习使用训练数据的排序函数，使得它独立于每个查询的相关文档的数量。

公开(公告)号：US08122015B2

公开(公告)日：2012-02-21

申请号：US11859066

申请日：2007-09-21

申请人： Tie-Yan Liu ,  Qin Tao ,  Hang Li

发明人： Tie-Yan Liu ,  Qin Tao ,  Hang Li

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06F17/3053

摘要： Systems and methods for processing user queries and identifying a set of documents relevant to the user query from a database using multi ranker search are described. In one implementation, the retrieved documents can be paired to form document pairs, or instance pairs, in a variety of combinations. Such instance pairs may have a rank order between them as they all have different ranks. A classifier, hyperplane, and a base ranker may be constructed for identifying the rank order relationships between the two instances in an instance pair. The base ranker may be generated for each rank pair. The systems use a divide and conquer strategy for learning to rank the instance pairs by employing multiple hyperplanes and aggregate the base rankers to form an ensemble of base rankers. Such an ensemble of base rankers can be used to rank the documents or instances.

摘要翻译： 描述了用于处理用户查询的系统和方法，以及使用多游标搜索从数据库识别与用户查询相关的一组文档。 在一个实现中，检索到的文档可以被配对以形成各种组合的文档对或实例对。 这样的实例对可以在它们之间具有排序，因为它们都具有不同的等级。 可以构造一个分类器，超平面和基本游标，用于识别实例对中的两个实例之间的排序关系。 可以为每个等级对生成基本杀手。 系统使用分裂和征服策略来学习通过使用多个超平面来对实例对进行排名，并且聚合基本等级以形成基本等级的组合。 可以使用这样一个基本排名的组合对文档或实例进行排名。

公开(公告)号：US07853599B2

公开(公告)日：2010-12-14

申请号：US12017288

申请日：2008-01-21

申请人： Tie-Yan Liu ,  Geng Xiubo ,  Tao Qin ,  Hang Li

发明人： Tie-Yan Liu ,  Geng Xiubo ,  Tao Qin ,  Hang Li

IPC分类号： G06F17/30

CPC分类号： G06F17/30675

摘要： This disclosure describes various exemplary methods, computer program products, and systems for selecting features for ranking in information retrieval. This disclosure describes calculating importance scores for features, measuring similarity scores between two features, selecting features that maximizes total importance scores of the features and minimizes total similarity scores between the features. Also, the disclosure includes selecting features for ranking that solves an optimization problem. Thus, this disclosure identifies relevant features by removing noisy and redundant features and speeds up a process of model training.

摘要翻译： 本公开描述了各种示例性方法，计算机程序产品和用于选择用于在信息检索中排名的特征的系统。 该公开内容描述了计算特征的重要度得分，测量两个特征之间的相似性得分，选择使特征的总重要度得分最大化的特征并使特征之间的总相似性得分最小化的特征。 此外，本公开包括选择解决优化问题的排名特征。 因此，本公开通过去除噪声和冗余特征并加速模型训练的过程来识别相关特征。

公开(公告)号：US07840522B2

公开(公告)日：2010-11-23

申请号：US11682963

申请日：2007-03-07

申请人： Tie-Yan Liu ,  Hang Li ,  Yu-Ting Liu

发明人： Tie-Yan Liu ,  Hang Li ,  Yu-Ting Liu

IPC分类号： G06F15/18

CPC分类号： G06F17/3053 ,  G06N7/005 ,  Y10S707/99931 ,  Y10S707/99933

摘要： A method and system for rank aggregation of entities based on supervised learning is provided. A rank aggregation system provides an order-based aggregation of rankings of entities by learning weights within an optimization framework for combining the rankings of the entities using labeled training data and the ordering of the individual rankings. The rank aggregation system is provided with multiple rankings of entities. The rank aggregation system is also provided with training data that indicates the relative ranking of pairs of entities. The rank aggregation system then learns weights for each of the ranking sources by attempting to optimize the difference between the relative rankings of pairs of entities using the weights and the relative rankings of pairs of entities of the training data.

摘要翻译： 提供了一种基于监督学习的实体等级聚合的方法和系统。 排名聚合系统通过在优化框架内学习权重来提供实体排序的基于订单的聚合，以使用标记的训练数据和个体排名的顺序组合实体的排名。 排名聚合系统提供多个实体排名。 等级聚合系统还提供了指示实体对的相对排名的训练数据。 秩聚合系统然后通过尝试使用训练数据的实体对的权重和相对排名来优化实体对的相对排名之间的差异来学习每个排名来源的权重。

公开(公告)号：US20100169323A1

公开(公告)日：2010-07-01

申请号：US12344607

申请日：2008-12-29

申请人： Tie-Yan Liu ,  Xiubo Geng ,  Hang Li

发明人： Tie-Yan Liu ,  Xiubo Geng ,  Hang Li

IPC分类号： G06F17/30 ,  G06F7/00

CPC分类号： G06K9/6276 ,  G06F16/334

摘要： Described is a technology in which documents associated with a query are ranked by a ranking model that depends on the query. When a query is processed, a ranking model for the query is selected/determined based upon nearest neighbors to the query in query feature space. In one aspect, the ranking model is trained online, based on a training set obtained from a number of nearest neighbors to the query. In an alternative aspect, ranking models are trained offline using training sets; the query is used to find a most similar training set based on nearest neighbors of the query, with the ranking model that corresponds to the most similar training set being selected for ranking. In another alternative aspect, the ranking models are trained offline, with the nearest neighbor to the query determined and used to select its associated ranking model.

摘要翻译： 描述了一种技术，其中与查询相关联的文档由依赖于查询的排名模型排序。 当处理查询时，基于查询特征空间中查询的最近邻居来选择/确定查询的排名模型。 在一个方面，基于从查询的多个最近邻居获得的训练集，在线训练排名模型。 在替代方面，使用训练集离线训练排名模型; 该查询用于基于查询的最近邻居找到最相似的训练集，其中与最相似的训练集合对应的排名模型被选择用于排名。 在另一个替代方面，离线训练排序模型，确定查询的最近邻，并用于选择其相关联的排名模型。

公开(公告)号：US20100082639A1

公开(公告)日：2010-04-01

申请号：US12242657

申请日：2008-09-30

申请人： Hang Li ,  Tie-Yan Liu

发明人： Hang Li ,  Tie-Yan Liu

IPC分类号： G06F17/30

CPC分类号： G06F16/334

摘要： The present invention introduces a new approach to learning systems. More specifically, the present invention provides learned methods for optimize ranking models. In one aspect of the present invention, an objective function is defined as the likelihood of ground truth based on a Luce model. In another aspect, techniques of the present invention provide a way of representing different kinds of ground truths as a constraint set of permutations. In yet another aspect of the present invention, techniques of the present invention provide a way of learning the model parameter by maximizing the likelihood of the ground truth.

摘要翻译： 本发明引入了一种学习系统的新方法。 更具体地，本发明提供了用于优化排名模型的学习方法。 在本发明的一个方面中，目标函数被定义为基于Luce模型的地面真实的可能性。 在另一方面，本发明的技术提供了将不同种类的地面真值表示为排列的约束集的方式。 在本发明的另一方面，本发明的技术提供了通过最大化地面真相的可能性来学习模型参数的方式。