Automatically generated comparison polls

    公开(公告)号:US09684908B2

    公开(公告)日:2017-06-20

    申请号:US14449390

    申请日:2014-08-01

    Applicant: Yahoo!, Inc.

    CPC classification number: G06Q30/02

    Abstract: A content item presented to a user may involve two or more topics, such as news articles mentioning athletes or actors. Users may enjoy expressing opinions about a comparison of the topics, such as personal preferences or an evaluation of talent. Presented herein are techniques for automatically generating, for the content item, a poll among the topics of the content item. The respective comparisons of a comparison set are evaluated for the topics of the content item, and a comparison relevance score is computed identifying the relevance of the comparison of the topics. The comparison having the highest relevance for the topics is selected, and a comparison question is formulated and presented to the user with the content item. Results may be tabulated and presented for the topics of the content item. These techniques facilitate user expression about the content item without depending upon user-authored polls for each content item.

    Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization
    2.
    发明授权
    Efficient and fault-tolerant distributed algorithm for learning latent factor models through matrix factorization 有权
    通过矩阵分解学习潜在因子模型的有效和容错分布式算法

    公开(公告)号:US09535938B2

    公开(公告)日:2017-01-03

    申请号:US14123259

    申请日:2013-03-15

    Applicant: Yahoo! Inc.

    CPC classification number: G06F17/30312 G06Q30/0631 G09B19/00

    Abstract: A method for estimating model parameters. The method comprises receiving a data set related to a plurality of users and associated content, partitioning the data set into a plurality of sub data sets in accordance with the users so that data associated with each user are not partitioned into more than one sub data set, storing each of the sub data sets in a separate one of a plurality of user data storages, each of said data storages being coupled with a separate one of a plurality of estimators, storing content associated with the plurality of users in a content storage, where the content storage is coupled to the plurality of estimators so that the content in the content storage is shared by the estimators, and estimating, asynchronously by each estimator, one or more parameters associated with a model based on data from one of the sub data sets.

    Abstract translation: 一种估计模型参数的方法。 该方法包括:接收与多个用户相关的数据集和相关联的内容,根据用户将数据集划分成多个子数据集,使得与每个用户相关联的数据不被划分成多个子数据集 将每个子数据集存储在多个用户数据存储器中的单独一个中,每个所述数据存储器与多个估计器中的单独一个估计器耦合,将与多个用户相关联的内容存储在内容存储器中, 其中所述内容存储器耦合到所述多个估计器,使得所述内容存储器中的内容由所述估计器共享,并且由每个估计器异步地估计与来自所述子数据之一的数据的与模型相关联的一个或多个参数 套。

    AUTOMATICALLY GENERATED COMPARISON POLLS
    3.
    发明申请
    AUTOMATICALLY GENERATED COMPARISON POLLS 有权
    自动生成比较POLLS

    公开(公告)号:US20160034585A1

    公开(公告)日:2016-02-04

    申请号:US14449390

    申请日:2014-08-01

    Applicant: Yahoo!, Inc.

    CPC classification number: G06Q30/02

    Abstract: A content item presented to a user may involve two or more topics, such as news articles mentioning athletes or actors. Users may enjoy expressing opinions about a comparison of the topics, such as personal preferences or an evaluation of talent. Presented herein are techniques for automatically generating, for the content item, a poll among the topics of the content item. The respective comparisons of a comparison set are evaluated for the topics of the content item, and a comparison relevance score is computed identifying the relevance of the comparison of the topics. The comparison having the highest relevance for the topics is selected, and a comparison question is formulated and presented to the user with the content item. Results may be tabulated and presented for the topics of the content item. These techniques facilitate user expression about the content item without depending upon user-authored polls for each content item.

    Abstract translation: 呈现给用户的内容项目可以涉及两个或多个主题,例如提及运动员或演员的新闻文章。 用户可以对个人偏好或人才评估等主题进行比较,表达意见。 这里提出的是用于为内容项目自动生成内容项目的主题之间的轮询的技术。 针对内容项的主题评估对比集合的各自比较,并且计算比较相关性得分,以识别主题的比较的相关性。 选择与主题相关性最高的比较,并且对用户提供比较问题并呈现给内容项。 可以将结果列表并呈现给内容项的主题。 这些技术有助于用户对内容项的表达,而不依赖于每个内容项的用户创建的轮询。

    QUALITY-BASED SCORING AND INHIBITING OF USER-GENERATED CONTENT

    公开(公告)号:US20200090062A1

    公开(公告)日:2020-03-19

    申请号:US16693825

    申请日:2019-11-25

    Applicant: Yahoo! Inc.

    Abstract: Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.

    ALMOST ONLINE LARGE SCALE COLLABORATIVE FILTERING BASED RECOMMENDATION SYSTEM
    5.
    发明申请
    ALMOST ONLINE LARGE SCALE COLLABORATIVE FILTERING BASED RECOMMENDATION SYSTEM 有权
    在线大型基于协同过滤的建议系统

    公开(公告)号:US20140280251A1

    公开(公告)日:2014-09-18

    申请号:US14123321

    申请日:2013-03-15

    Applicant: YAHOO! Inc.

    CPC classification number: G06F17/30867 G06Q30/0269

    Abstract: A method for adjusting one or more parameters associated with a model. The method comprises obtaining, from a first source, first information related to activity of a user. The method further comprises adjusting one or more parameters associated with a model based on the first information collected within a first length of time, and obtaining, from a second source, second information related to activity of the user. The method further comprises adjusting the one or more parameters associated with the model based on the second information collected within a second length of time and a measure indicative of performance of the model, wherein the second length of time is larger than the first length of time.

    Abstract translation: 一种用于调整与模型相关联的一个或多个参数的方法。 该方法包括从第一来源获得与用户的活动相关的第一信息。 该方法还包括基于在第一时间长度内收集的第一信息调整与模型相关联的一个或多个参数,以及从第二源获得与用户的活动相关的第二信息。 该方法还包括基于在第二时间长度内收集的第二信息和指示模型性能的量度来调整与模型相关联的一个或多个参数,其中第二时间长度大于第一时间长度 。

    Almost online large scale collaborative filtering based recommendation system
    8.
    发明授权
    Almost online large scale collaborative filtering based recommendation system 有权
    几乎在线大型协同过滤推荐系统

    公开(公告)号:US09348924B2

    公开(公告)日:2016-05-24

    申请号:US14123321

    申请日:2013-03-15

    Applicant: Yahoo! Inc.

    CPC classification number: G06F17/30867 G06Q30/0269

    Abstract: A method for adjusting one or more parameters associated with a model. The method comprises obtaining, from a first source, first information related to activity of a user. The method further comprises adjusting one or more parameters associated with a model based on the first information collected within a first length of time, and obtaining, from a second source, second information related to activity of the user. The method further comprises adjusting the one or more parameters associated with the model based on the second information collected within a second length of time and a measure indicative of performance of the model, wherein the second length of time is larger than the first length of time.

    Abstract translation: 一种用于调整与模型相关联的一个或多个参数的方法。 该方法包括从第一来源获得与用户的活动相关的第一信息。 该方法还包括基于在第一时间长度内收集的第一信息调整与模型相关联的一个或多个参数,以及从第二来源获得与用户活动相关的第二信息。 该方法还包括基于在第二时间长度内收集的第二信息和指示模型性能的量度来调整与模型相关联的一个或多个参数,其中第二时间长度大于第一时间长度 。

    METHOD AND SYSTEM FOR COLD-START ITEM RECOMMENDATION
    9.
    发明申请
    METHOD AND SYSTEM FOR COLD-START ITEM RECOMMENDATION 审中-公开
    用于加速项目建议的方法和系统

    公开(公告)号:US20160110646A1

    公开(公告)日:2016-04-21

    申请号:US14519273

    申请日:2014-10-21

    Applicant: Yahoo! Inc.

    Abstract: Method, system, and programs for estimating interests of a plurality of users with respect to a new piece of information are disclosed. In one example, historical interests of the plurality of users are obtained with respect to one or more existing pieces of information. One or more users are selected from the plurality of users. Historical interests of the one or more users can minimize an objective function over the plurality of users. Interests of the one or more users are obtained with respect to the new piece of information. Estimated interests of the plurality of users are generated with respect to the new piece of information based on the obtained interests of the one or more users.

    Abstract translation: 公开了用于估计关于新信息的多个用户的兴趣的方法,系统和程序。 在一个示例中,针对一个或多个现有的信息获得多个用户的历史兴趣。 从多个用户中选择一个或多个用户。 一个或多个用户的历史兴趣可以使多个用户的目标功能最小化。 获得关于新的信息的一个或多个用户的兴趣。 基于获得的一个或多个用户的兴趣,针对新的信息片段生成多个用户的估计兴趣。

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