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公开(公告)号：US20170193106A1
公开(公告)日：2017-07-06
申请号：US14983663
申请日：2015-12-30
Applicant: Yahoo! Inc.
Inventor： Shiyu Chang , Jiliang Tang , Dawei Yin , Yi Chang
CPC classification number: G06F16/735 , G06F16/24578 , G06F16/435 , G06F16/738 , G06F16/78 , G06F16/90324 , H04L67/02 , H04L67/1085
Abstract: The present teaching relates to recommending content by analyzing the streamed data. A request is received from a user requesting one or more recommendations from a set of items. A first distribution indicative of an interest distribution of the user in a plurality of topics is obtained. For each item, a second distribution indicative of a classification distribution of the item with respect to the plurality of topics is obtained. A score is estimated based on the first distribution and the second distribution, wherein the score indicates likelihood that the user is interested in the item. The scores associated with the set of items are ranked. The one or more recommendations are presented based on the ranked scores
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公开(公告)号：US20170091203A1
公开(公告)日：2017-03-30
申请号：US14868154
申请日：2015-09-28
Applicant: Yahoo! Inc.
Inventor： Yuening Hu , Changsung Kang , Dawei Yin , Yi Chang
IPC: G06F17/30
CPC classification number: G06F16/9537
Abstract: A location prediction framework is described for applying location labels or tags to target documents and/or identifying location-sensitive queries. Terms in content and queries are represented by corresponding term locations vectors (TLVs) in which the term is represented as a weighted distribution across locations. Each element of a TLV represents a probability that the term corresponding to the TLV relates to a particular location. Predicted locations may be introduced as features to a ranking framework to improve the identification and ranking of search results for a given query.
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公开(公告)号：US20170091198A1
公开(公告)日：2017-03-30
申请号：US14869435
申请日：2015-09-29
Applicant: YAHOO! INC.
Inventor： Lizi Zhang , Hongbo Deng , Amit Goyal , Yi Chang , Ricardo Baeza-Yates
CPC classification number: G06F17/3064 , G06F17/30654 , G06F17/30696 , G06Q30/0256
Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for determining and suggesting query auto-completions (QACs). In some embodiments, when a user is inputting a search query, the disclosed systems and methods can provide a QAC suggestion based on the inputted text in addition to application programs installed and/or executing on the user's device.
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公开(公告)号：US20160335263A1
公开(公告)日：2016-11-17
申请号：US14959122
申请日：2015-12-04
Applicant: Yahoo! Inc.
Inventor： Dawei Yin , Pengyuan Wang , Hua Ouyang , Yi Chang , Jean-Marc Langlois
IPC: G06F17/30
CPC classification number: G06F16/24578 , G06F16/9535
Abstract: The present teaching relates to ranking search content. In one example, a plurality of documents is received to be ranked with respect to a query. Features are extracted from the query and the plurality of documents. The plurality of documents is ranked based on a ranking model and the extracted features. The ranking model is derived to remove one or more documents from the plurality of documents that are less relevant to the query and order remaining documents based on their relevance to the query. The ordered remaining documents are provided as a search result with respect to the query.
Abstract translation: 本教学涉及排名搜索内容。 在一个示例中，接收到相对于查询进行排名的多个文档。 从查询和多个文档中提取特征。 基于排名模型和提取的特征来对多个文档进行排名。 导出排序模型，以从多个文档中删除与查询相关性较低的一个或多个文档，并根据其与查询的相关性来订购剩余的文档。 订购的剩余文件作为查询的搜索结果提供。
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公开(公告)号：US20160210367A1
公开(公告)日：2016-07-21
申请号：US14601128
申请日：2015-01-20
Applicant: Yahoo! Inc.
Inventor： Makoto Yamada , Yi Chang
CPC classification number: G06F16/9535 , G06Q30/0201
Abstract: Detection of one or more transition events.
Abstract translation: 检测一个或多个过渡事件。
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Patent Agency Ranking
公开(公告)号：US08880518B2
公开(公告)日：2014-11-04
申请号：US13662159
申请日：2012-10-26
Applicant: Yahoo! Inc.
Inventor： Yi Chang , Anlei Dong
Abstract: Techniques are described herein for enhancing the ranking products using purchase day based time windows. A purchase day based time window is a time window that is defined to include purchase days selected from a series of consecutive days. A purchase day is a day on which a product associated with the time window is purchased. The series of consecutive days includes the purchase days intermixed with non-purchase day(s). A non-purchase day is a day on which the product associated with the time window is not purchased. The purchase day based time window is further defined to not include the non-purchase day(s).
Abstract translation: 本文描述了使用基于购买日的时间窗来增强排名产品的技术。 基于购买日的时间窗口是一个时间窗口，其定义为包括从连续一天连续几天中选择的购买天数。 购买日是与时间窗口相关联的产品购买的日子。 连续一天包括与非购买日混合的购买日。 非购买日是与时间窗口关联的产品未被购买的日子。 基于购买日的时间窗口被进一步定义为不包括非购买日。
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7.发明申请公开(公告)号：US20160253598A1
公开(公告)日：2016-09-01
申请号：US14634515
申请日：2015-02-27
Applicant: Yahoo! Inc.
Inventor： Makoto Yamada , Chao Qin , Hua Ouyang , Achint Thomas , Yi Chang
IPC: G06N99/00
CPC classification number: G06N20/00 , G06F21/552
Abstract: In one embodiment, a set of training data consisting of inliers may be obtained. A supervised classification model may be trained using the set of training data to identify outliers. The supervised classification model may be applied to generate an anomaly score for a data point. It may be determined whether the data point is an outlier based, at least in part, upon the anomaly score.
Abstract translation: 在一个实施例中，可以获得由内联组成的一组训练数据。 可以使用该组训练数据来训练监督分类模型以识别异常值。 可以应用监督分类模型来产生数据点的异常得分。 可以至少部分地基于异常评分来确定数据点是否是异常值。
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公开(公告)号：US20140172652A1
公开(公告)日：2014-06-19
申请号：US13720703
申请日：2012-12-19
Applicant: YAHOO! INC.
Inventor： Nageswara R. Pobbathi , Anlei Dong , Yi Chang
IPC: G06Q10/08
CPC classification number: G06Q10/087
Abstract: A system and method is described for large-scale, automated classification of products. The system and method receives information about products, wherein such information includes one or more text metadata fields associated with each product, receives a set of categories, and automatically selects one or more categories from the set of categories to which each product belongs based upon at least one of the one or more text metadata fields associated with each product. A machine learning classifier may be used to automatically select the one or more categories to which each product belongs by operating upon a feature vector for each product derived from text metadata fields of the product description. The machine learning classifier may be trained using a set of pre-categorized product descriptions. The product-category associations generated by the system and method can be used to improve search engine results or product recommendations to consumers.
Abstract translation: 描述了用于产品的大规模自动分类的系统和方法。 系统和方法接收关于产品的信息，其中这样的信息包括与每个产品相关联的一个或多个文本元数据字段，接收一组类别，并且基于每个产品所属的一组类别自动选择一个或多个类别 与每个产品相关联的一个或多个文本元数据字段中的至少一个。 机器学习分类器可以用于通过对从产品描述的文本元数据字段导出的每个产品的特征向量进行操作来自动选择每个产品所属的一个或多个类别。 可以使用一组预先分类的产品描述来训练机器学习分类器。 系统和方法生成的产品类别关联可用于将消费者的搜索引擎结果或产品建议改进。
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9.发明申请公开(公告)号：US20170270122A1
公开(公告)日：2017-09-21
申请号：US15074028
申请日：2016-03-18
Applicant: YAHOO! INC.
Inventor： Yunlong He , Dawei Yin , Yi Chang
Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide a unified digital content discovery framework that implements a combination of a logistic loss function and a pair-wise loss function for information retrieval. The logistic loss function reduces non-relevant images from appearing in the retrieved results, while the pair-wise loss function ensures that the highest-quality content is included in such results. The combination of such functions provides a search information retrieval system with the novel functionality of quantifying a search results' relevance and quality in accordance with the searcher's intent.
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公开(公告)号：US20160378858A1
公开(公告)日：2016-12-29
申请号：US15261546
申请日：2016-09-09
Applicant: Yahoo! Inc.
Inventor： Srinivas Vadrevu , Yi Chang , Zhaohui Zheng , Bo Long
IPC: G06F17/30
CPC classification number: G06F16/35 , G06F16/334 , G06F16/338 , G06F16/93
Abstract: One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.
Abstract translation: 一个特定实施例使用一个或多个聚类算法来聚集多个文档以获得一个或多个第一组聚类，其中：每个第一组聚类是使用聚类算法之一聚类文档而得到的; 并且对于每个第一组集合，每个文档属于来自第一组集合的集群之一; 访问搜索查询; 识别响应于搜索查询的搜索结果，其中搜索结果包括两个或更多个文档; 并且聚集搜索结果以获得第二组聚类，其中搜索结果的每个文档属于来自第二组聚类的聚类中的一个。
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