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    • 1. 发明授权
    • Recommending queries according to mapping of query communities
    • 根据查询社区的映射推荐查询
    • US09171045B2
    • 2015-10-27
    • US12943951
    • 2010-11-11
    • Nina MishraSreenivas GollapudiSrikanth Jagabathula
    • Nina MishraSreenivas GollapudiSrikanth Jagabathula
    • G06F17/30
    • G06F17/30528G06F17/3097
    • A set of queries, such as a search log, is divided into commercial queries and non-commercial queries. A first set of query communities is determined from the non-commercial queries and a second set is determined from the commercial queries. The query communities are correlated based on the users who submitted the queries and instances where a query from the first set of query communities was followed by a query from the second set to generate a mapping between the first set of query communities and the second set. Later, a non-commercial query is received from a user, and the mapping is used to predict one or more commercial queries that the user is likely to submit in the future based on the non-commercial query. One or more of the commercial queries are presented to the user according to the mapping with search results responsive to the non-commercial query.
    • 诸如搜索日志的一组查询被分为商业查询和非商业查询。 从非商业查询确定第一组查询社区,并从商业查询中确定第二组。 查询社区是基于提交查询的用户和来自第一组查询社区的查询后跟来自第二组的查询的用户相关联的,以生成第一组查询社区和第二组之间的映射。 之后,从用户接收到非商业查询,并且映射用于基于非商业查询来预测用户将来可能提交的一个或多个商业查询。 根据与响应于非商业查询的搜索结果的映射,向用户呈现一个或多个商业查询。
    • 2. 发明授权
    • Determining query intent
    • 确定查询意图
    • US08612432B2
    • 2013-12-17
    • US12816389
    • 2010-06-16
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • G06F7/00G06F17/30G06F15/18
    • G06F17/30979
    • A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.
    • 树结构具有与项目类别的层次结构的每个类别相关联的节点。 树的子节点与与父节点相关联的类别的子类别相关联。 通过将每个查询与对应于所选择的查询类别的节点相关联,将包括接收到的查询和针对每个接收到的查询的所选项目类别的指示符的训练数据与树结构组合。 当接收到查询时,分类器被应用于节点以产生查询旨在匹配与节点相关联的类别的项目的概率。 应用分类器直到概率低于阈值。 选择与接收到的查询的意图最接近的节点相关联的一个或多个类别,并输出与接收到的查询匹配的那些类别的项目的指示符。
    • 4. 发明授权
    • Method and apparatus for document clustering and document sketching
    • 用于文档聚类和文档素描的方法和装置
    • US08255397B2
    • 2012-08-28
    • US12198841
    • 2008-08-26
    • Sreenivas Gollapudi
    • Sreenivas Gollapudi
    • G06F17/30
    • G06F17/30616G06F17/3071Y10S707/99933Y10S707/99935Y10S707/99942Y10S707/99943
    • A first embodiment of the invention provides a system that automatically classifies documents in a collection into clusters based on the similarities between documents, that automatically classifies new documents into the right clusters, and that may change the number or parameters of clusters under various circumstances. A second embodiment of the invention provides a technique for comparing two documents, in which a fingerprint or sketch of each document is computed. In particular, this embodiment of the invention uses a specific algorithm to compute the document's fingerprint. One embodiment uses a sentence in the document as a logical delimiter or window from which significant words are extracted and, thereafter, a hash is computed of all pair-wise permutations. Words are extracted based on their weight in the document, which can be computed using measures such as term frequency and the inverse document frequency.
    • 本发明的第一实施例提供了一种系统,其基于文档之间的相似性自动地将集合中的文档分类成簇,该文档将新文档自动分类到正确的集群中,并且可以在各种情况下改变集群的数量或参数。 本发明的第二实施例提供了一种用于比较两个文档的技术,其中计算每个文档的指纹或草图。 特别地,本发明的该实施例使用特定算法来计算文档的指纹。 一个实施例将文档中的句子用作提取有效字的逻辑定界符或窗口,此后,计算所有成对排列的散列。 根据文档中的权重提取单词,可以使用诸如术语频率和逆文档频率等度量来计算单词。
    • 5. 发明申请
    • EFFECTIVE AD PLACEMENT
    • 有效的放置
    • US20110258033A1
    • 2011-10-20
    • US12760572
    • 2010-04-15
    • Sreenivas GollapudiRina Panigrahy
    • Sreenivas GollapudiRina Panigrahy
    • G06Q30/00G06F17/30
    • G06Q30/0244G06F16/972G06Q30/02G06Q30/0243
    • Search results and ads that satisfy a query may be formatted into a search results page where an initial placement of ads may result in an estimated click through rate for the presented ads. An adjustment factor may be determined based on parameters associated with the search results, such as the clicks on the search results themselves. The adjustment factor may be applied to a particular ad to determine if an estimated click through rate of the particular ad will change with respect to a position when there are a first number of mainline ads and a second number of side bar ads. Mainline exclusivity may be appropriate for an ad to increase the click through rate of the ad. The increase may be determined in accordance with the adjustment factor to decide whether to present the ad with mainline exclusivity.
    • 满足查询的搜索结果和广告可能格式化为搜索结果页面,其中初始展示位置可能会导致展示广告的估算点击率。 可以基于与搜索结果相关联的参数(例如搜索结果本身的点击)来确定调整因子。 调整因子可以应用于特定广告,以确定当存在第一数量的主线广告和第二数量的边栏广告时,特定广告的估计点击率是否相对于该位置而改变。 主线排他性可能适合于增加广告点击率的广告。 该增加可以根据调整因素来确定是否呈现具有主线排他性的广告。
    • 8. 发明申请
    • LARGE GRAPH MEASUREMENT
    • 大图测量
    • US20100228731A1
    • 2010-09-09
    • US12396514
    • 2009-03-03
    • Sreenivas Gollapudi
    • Sreenivas Gollapudi
    • G06F17/30G06N5/02
    • G06F17/30876G06F17/30873
    • As provided herein, a pairwise distance between nodes in a large graph can be determined efficiently. URL-sketches are generated for respective nodes in an index by extracting labels from respective nodes, which provide a reference to a link between the nodes, aggregating the labels into sets for respective nodes, and storing the sets of labels as URL-sketches. Neighborhood-sketches are generated for the respective nodes in the index using the URL-sketches, by determining a neighborhood for a node and generating a sketch using labels that are associated with the respective neighboring nodes. A distance between two nodes is determined by computing an approximate number of paths and an approximate path length between the two nodes, using the neighborhood sketches for the two nodes.
    • 如本文所提供的,可以有效地确定大图中的节点之间的成对距离。 通过从相应节点提取标签,提供对节点之间的链接的引用,将标签聚集到各个节点的集合中,并将标签集合存储为URL草图,从而为索引中的相应节点生成URL草图。 通过使用URL草图,通过确定节点的邻域并使用与各个相邻节点相关联的标签生成草图来为索引中的相应节点生成邻域草图。 通过使用两个节点的邻域草图计算两个节点之间的近似路径数和两个节点之间的近似路径长度来确定两个节点之间的距离。
    • 9. 发明授权
    • Method of finding candidate sub-queries from longer queries
    • 从较长查询中查找候选子查询的方法
    • US07765204B2
    • 2010-07-27
    • US11863045
    • 2007-09-27
    • Sreenivas GollapudiRina Panigrahy
    • Sreenivas GollapudiRina Panigrahy
    • G06F17/30
    • G06F17/30693G06F17/30457
    • A method is disclosed for identifying queries stored in a log which are semantically related to an input query that may include a large number of terms. A set of one or more subsequences are generated for each query stored in the log, and these sets of subsequences are stored in a lookup table. A set of one or more subsequences are also generated for the input query. The subsequences in the lookup table and of the input query are generated by hashing of the respective query terms to a value between 0 and 1 using a known technique of min-hashing. The present system then constructs the subsequences of the query using the k-min hashes of the query, where k is an integer based on the number of terms in the query.
    • 公开了一种用于识别存储在日志中的查询与可能包括大量项目的输入查询语义相关的查询的方法。 为存储在日志中的每个查询生成一组或多个子序列,并将这些子序列存储在查找表中。 还为输入查询生成一组或多个子序列。 查询表和输入查询中的子序列通过使用最小哈希算法的已知技术将相应的查询项散列为0到1之间的值来生成。 本系统然后使用查询的k-min哈希构建查询的子序列,其中k是基于查询中的项数的整数。