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公开(公告)号：US20170300530A1
公开(公告)日：2017-10-19
申请号：US15130217
申请日：2016-04-15
Applicant: Yahoo! Inc.
Inventor： Jiliang Tang , Dawei Yin , Hongbo Deng , Tim Daly , Chao Tan , Jean-Marc Langlois , Yi Chang
CPC classification number: G06F16/24534 , G06F16/24578 , G06N20/00
Abstract: The present teaching relates to rewriting a query and providing search results. In one example, a plurality of queries is obtained. For each of the plurality of queries, one or more search results are identified. The one or more search results have been obtained in response to the query and have been previously selected by a user submitting the query. A plurality of titles is obtained. Each of the titles corresponds to one of the one or more search results with respect to one of the plurality of queries. A model is generated based on the plurality of queries and the plurality of titles. The model is to be used for rewriting a query.
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2.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：US20180005131A1
公开(公告)日：2018-01-04
申请号：US15200922
申请日：2016-07-01
Applicant: Yahoo! Inc.
Inventor： Dawei Yin , Jiliang Tang , Yi Chang
CPC classification number: G06N7/005 , G06F16/9535 , G06N20/00 , G06Q50/01
Abstract: Disclosed are systems and methods for improving interactions with and between computers in social media content generation and delivery 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 automatically method for automatically summarizing social media content using a timeline comprising a set (or chain) of episodes and a summary of each episode. The disclosed systems and methods identify a number of episodes based on analysis of each social media content item of a corpus, identify a number of social content items to summarize each episode, and generate a timeline summarization of the corpus of social media content items.
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Patent Agency Ranking
公开(公告)号：US20170091189A1
公开(公告)日：2017-03-30
申请号：US14868135
申请日：2015-09-28
Applicant: Yahoo! Inc.
Inventor： Changsung Kang , Yuening Hu , Dawei Yin , Yi Chang
IPC: G06F17/30
CPC classification number: G06F16/24578 , G06F16/23 , G06F16/24575 , G06F16/29 , G06F16/9535
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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5.发明申请公开(公告)号：US20160189202A1
公开(公告)日：2016-06-30
申请号：US14587328
申请日：2014-12-31
Applicant: Yahoo! Inc.
Inventor： Pengyuan WANG , Dawei Yin , Yi Chang , Jian Yang , Wei Sun
CPC classification number: G06Q30/0243 , G06Q10/067
Abstract: Systems and methods for are provided for measuring treatment effect of advertisement campaigns. The system includes a processor and a non-transitory storage medium accessible to the processor. The system includes a memory storing a database including historical advertisement data. A computer server is in communication with the memory and the database, the computer server programmed to obtain a tree-based model using the historical advertisement data, where the tree-based model include a plurality of leaf nodes. Within at least one leaf node of the tree-based model, the computer server obtains a number of subjects and estimates a treatment effect for a treatment. The computer server calculates a final treatment effect for the tree-based model using the number of subjects and the treatment effect. The computer server then determines a parameter for future advertising strategy using the final treatment effect.
Abstract translation: 提供用于衡量广告活动的治疗效果的系统和方法。 该系统包括可处理器可访问的处理器和非暂时性存储介质。 该系统包括存储包含历史广告数据的数据库的存储器。 计算机服务器与存储器和数据库通信，计算机服务器被编程为使用历史广告数据获得基于树的模型，其中基于树的模型包括多个叶节点。 在基于树的模型的至少一个叶节点中，计算机服务器获得多个受试者并估计治疗的治疗效果。 计算机服务器使用受试者数量和治疗效果计算基于树型模型的最终治疗效果。 然后，计算机服务器使用最终处理效果确定用于将来广告策略的参数。
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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：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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