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1. 发明授权

US08122015B2 Multi-ranker for search 有权
标题翻译：多人游戏搜索
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。

2. 发明授权

US07853599B2 Feature selection for ranking 失效
标题翻译：功能选择排名
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。

3. 发明授权

US07840522B2 Supervised rank aggregation based on rankings 有权
标题翻译：基于排名的监督排名聚合
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。

4. 发明申请

US20100169323A1 Query-Dependent Ranking Using K-Nearest Neighbor 审中-公开
标题翻译：使用K最近邻的查询依赖排名
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。

5. 发明申请

US20100082639A1 PROCESSING MAXIMUM LIKELIHOOD FOR LISTWISE RANKINGS 审中-公开
标题翻译：处理列表排名的最大比例
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。

6. 发明申请

US20100082606A1 DIRECTLY OPTIMIZING EVALUATION MEASURES IN LEARNING TO RANK 有权
标题翻译：直接优化评估评估方法
公开(公告)号：US20100082606A1
公开(公告)日：2010-04-01
申请号：US12237293
申请日：2008-09-24
申请人： Jun Xu , Tie-Yan Liu , Hang Li
发明人： Jun Xu , Tie-Yan Liu , Hang Li
IPC分类号： G06F17/30 , G06F17/10
CPC分类号： G06F17/30687 , G06F17/30867
摘要： The present invention provides methods for improving a ranking model. In one embodiment, a method includes the step of obtaining queries, documents, and document labels. The process then initializes active sets using the document labels, wherein two active sets are established for each query, a perfect active set and an imperfect active set. Then, the process optimizes an empirical loss function by the use of the first and second active set, whereby parameters of the ranking model are modified in accordance to the empirical loss function. The method then updates the active sets with additional ranking data, wherein the updates are configured to work in conjunction with the optimized loss function and modified ranking model. The recalculated active sets provide an indication for ranking the documents in a way that is more consistent with the document metadata.
摘要翻译：本发明提供了改进排名模型的方法。在一个实施例中，一种方法包括获得查询，文档和文档标签的步骤。然后，该过程使用文档标签来初始化活动集合，其中为每个查询建立两个活动集合，完美的活动集合和不完全的活动集合。然后，该过程通过使用第一和第二活动集来优化经验损失函数，由此根据经验损失函数修改排名模型的参数。然后，该方法用附加排名数据更新活动集合，其中更新被配置为与优化的损失函数和修改的排名模型一起工作。重新计算的活动集提供了以与文档元数据更一致的方式对文档进行排名的指示。

7. 发明申请

US20100076910A1 CALCULATING WEB PAGE IMPORTANCE BASED ON WEB BEHAVIOR MODEL 有权
标题翻译：基于网络行为模型计算网页重要性
公开(公告)号：US20100076910A1
公开(公告)日：2010-03-25
申请号：US12237392
申请日：2008-09-25
申请人： Bin Gao , Tie-Yan Liu , Hang Li , Yuting Liu
发明人： Bin Gao , Tie-Yan Liu , Hang Li , Yuting Liu
IPC分类号： G06F17/15 , G06N5/02 , G06F17/11 , G06F3/048
CPC分类号： G06F17/30864 , G06Q30/02
摘要： Method for determining a webpage importance, including receiving web browsing behavior data of one or more users; creating a model of the web browsing behavior data; calculating a stationary probability distribution of the model; and correlating the stationary probability distribution to the webpage importance.
摘要翻译：用于确定网页重要性的方法，包括接收一个或多个用户的网页浏览行为数据; 创建网络浏览行为数据的模型; 计算模型的固定概率分布; 并将固定概率分布与网页重要性相关联。

8. 发明授权

US07593934B2 Learning a document ranking using a loss function with a rank pair or a query parameter 有权
标题翻译：使用具有排名对或查询参数的损失函数学习文档排名
公开(公告)号：US07593934B2
公开(公告)日：2009-09-22
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： G06F7/00 , G06F17/30 , G06F15/00
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。

9. 发明授权

US08103599B2 Calculating web page importance based on web behavior model 有权
标题翻译：基于Web行为模型计算网页重要性
公开(公告)号：US08103599B2
公开(公告)日：2012-01-24
申请号：US12237392
申请日：2008-09-25
申请人： Bin Gao , Tie-Yan Liu , Hang Li , Yuting Liu
发明人： Bin Gao , Tie-Yan Liu , Hang Li , Yuting Liu
IPC分类号： G06F17/00 , G06F17/20
CPC分类号： G06F17/30864 , G06Q30/02
摘要： Method for determining a webpage importance, including receiving web browsing behavior data of one or more users; creating a model of the web browsing behavior data; calculating a stationary probability distribution of the model; and correlating the stationary probability distribution to the webpage importance.
摘要翻译：用于确定网页重要性的方法，包括接收一个或多个用户的网页浏览行为数据; 创建网络浏览行为数据的模型; 计算模型的固定概率分布; 并将固定概率分布与网页重要性相关联。

10. 发明授权

US07930303B2 Calculating global importance of documents based on global hitting times 失效
标题翻译：根据全球打击时间计算文件的全球重要性
公开(公告)号：US07930303B2
公开(公告)日：2011-04-19
申请号：US11742276
申请日：2007-04-30
申请人： Tie-Yan Liu , Hang Li , Lei Qi , Bin Gao
发明人： Tie-Yan Liu , Hang Li , Lei Qi , Bin Gao
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30864
摘要： A calculate importance system calculates the global importance of a web page based on a “mean hitting time.” Hitting time of a target web page is a measure of the minimum number of transitions needed to land on the target web page. Mean hitting time of a target web page is an average number of such transitions for all possible starting web pages. The calculate importance system calculates a global importance score for a web page based on the reciprocal of a mean hitting time. A search engine may rank web pages of a search result based on a combination of relevance of the web pages to the search request and global importance of the web pages based on a global hitting time.
摘要翻译：计算重要度系统基于“平均打击时间”计算网页的全局重要性。目标网页的打击时间是衡量目标网页上所需的最小转换次数的度量。目标网页的平均打击时间是所有可能的起始网页的平均数量。计算重要性系统基于平均击球时间的倒数计算网页的全局重要性得分。搜索引擎可以基于网页与搜索请求的相关性和基于全局打击时间的网页的全球重要性的组合来对搜索结果的网页进行排序。

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