公开(公告)号：US20160125028A1

公开(公告)日：2016-05-05

申请号：US14533405

申请日：2014-11-05

Applicant: Yahoo! Inc.

Inventor： Fabrizio Silvestri ,  Mihajlo Grbovic ,  Narayan Bhamidipati ,  Vladan Radosavljevic ,  Nemanja Djuric

IPC: G06F17/30

CPC classification number: G06F16/3338

Abstract: Systems and methods for rewriting query terms are disclosed. The system collects queries and query session data and separates the queries into sequences of queries having common sessions. The sequences of queries are then input into a deep learning network to build a multidimensional word vector in which related terms are nearer one another than unrelated terms. An input query is then received and the system matches the input query in the multidimensional word vector and rewrites the query using the nearest neighbors to the term of the input query.

Abstract translation: 披露了用于重写查询术语的系统和方法。 系统收集查询并查询会话数据，并将查询分成具有公共会话的查询序列。 然后将查询序列输入到深度学习网络中以建立多维词向量，其中相关词相对于无关术语彼此更接近。 然后接收输入查询，并且系统与多维字向量中的输入查询匹配，并使用输入查询项中的最近邻居重写查询。

公开(公告)号：US20160170982A1

公开(公告)日：2016-06-16

申请号：US14572579

申请日：2014-12-16

Applicant: Yahoo! Inc.

Inventor： Nemanja Djuric ,  Vladan Radosavljevic ,  Hao Wu ,  Mihajlo Grbovic ,  Narayan Bhamidipati

IPC: G06F17/30 ,  G06N3/04

CPC classification number: G06F16/353 ,  G06F16/3347 ,  G06N3/0454

Abstract: The present teaching relates to joint representation of information. In one example, first and second pieces of information are received. Each of the first and second pieces of information relates to one word in a plurality of documents, one of the documents, or one of user to which the documents are given. A model for estimating feature vectors is obtained. The model includes a first neural network model based on a first order of words within one of the documents and a second neural network model based on a second order in which at least some of the documents are given. Based on the model, a first feature vector of the first piece of information and a second feature vector of the second piece of information are estimated. A similarity between the first and second pieces of information is determined based on a distance between the first and second feature vectors.

Abstract translation: 目前的教学与信息联合表达有关。 在一个示例中，接收第一和第二信息。 第一和第二信息中的每一个涉及多个文档中的一个单词，文档中的一个或给出文档的用户中的一个。 获得用于估计特征向量的模型。 该模型包括基于文档之一内的单词的第一顺序的第一神经网络模型和基于给出至少一些文档的第二次序的第二神经网络模型。 基于该模型，估计第一信息的第一特征向量和第二信息的第二特征向量。 基于第一和第二特征向量之间的距离确定第一和第二信息之间的相似度。

公开(公告)号：US20150379571A1

公开(公告)日：2015-12-31

申请号：US14320048

申请日：2014-06-30

Applicant: YAHOO! Inc.

Inventor： Mihajlo Grbovic ,  Nemanja Djuric ,  Vladan Radosavljevic ,  Narayan Bhamidipati

IPC: G06Q30/02 ,  G06F17/30

CPC classification number: G06Q30/0256

Abstract: A system stored in a non-transitory medium executable by processor circuitry is provided for generating retargeting keywords based on distributed query word representations. The system includes one or more system databases storing historical web search data. Search retargeting circuitry receives requests to generate sets of retargeting keywords related to one or more categories of an advertisement campaign and pre-processing circuitry retrieves a set of historical web search data related to the one or more categories of the advertisement campaign. Modeling circuitry further applies one or more computational linguistic models to the retrieved set of historical web search data and generates distributed query word representations from the retrieved set of historical web search data. Keyword generator circuitry generates a list of retargeting keywords related to the one or more categories of the advertisement campaign using the generated distributed query word representations.

Abstract translation: 提供了存储在可由处理器电路执行的非暂时介质中的系统，用于基于分布式查询词表示来生成重定位关键字。 该系统包括存储历史网络搜索数据的一个或多个系统数据库。 搜索重定向电路接收生成与一个或多个类别的广告活动相关的重定向关键词集的请求，并且预处理电路检索与一个或多个类别的广告活动相关的一组历史网络搜索数据。 建模电路还将一个或多个计算语言模型应用于所检索的历史网络搜索数据集合，并从检索到的一组历史网络搜索数据生成分布式查询词表示。 关键字生成器电路使用生成的分布式查询词表示来生成与广告活动的一个或多个类别相关的重定向关键词列表。