利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

36 条记录 (耗时 0.025 秒)

    Measuring Impact Of Online Advertising Campaigns
    1.
    发明申请
    Measuring Impact Of Online Advertising Campaigns 审中-公开
    衡量网络广告运动的影响

    公开(公告)号：US20100306043A1

    公开(公告)日：2010-12-02

    申请号：US12472318

    申请日：2009-05-26

    申请人： Robert Taaffe Lindsay ,  Thomas Carriero ,  Yun-Fang Juan

    发明人： Robert Taaffe Lindsay ,  Thomas Carriero ,  Yun-Fang Juan

    IPC分类号： G06Q30/00

    CPC分类号： G06Q30/0242 ,  G06Q30/02 ,  G06Q30/0245 ,  H04L67/306

    摘要： Methods and systems allow measurement of effectiveness of advertising campaigns based on online advertisements targeted towards specific sets of members. A set of members is allowed to see an advertisement whereas another set of members is withheld from seeing the advertisement under conditions similar to the first set. Test sets obtained from the two sets of members are polled with questions evaluating effectiveness of advertisement. The poll questions evaluate effectiveness based on factors including brand awareness, purchase intent, or brand favorability. Statistical analysis is performed to quantitatively measure the effectiveness of the advertisement by measuring the improvement in above factors as a result of showing the advertisement.

    摘要翻译： 方法和系统允许基于针对特定成员组的在线广告来衡量广告活动的有效性。 允许一组成员看到广告，而另一组成员在类似于第一组的条件下被拒绝看待广告。 从两组成员获得的测试集，对评估广告效果的问题进行了轮询。 投票问题基于品牌意识，购买意向或品牌喜好等因素来评估有效性。 进行统计分析，通过测量广告的结果，通过测量上述因素的改善来量化广告的有效性。

    Clustering a user's connections in a social networking system
    2.
    发明授权
    Clustering a user's connections in a social networking system 有权

    公开(公告)号：US09846916B2

    公开(公告)日：2017-12-19

    申请号：US13179547

    申请日：2011-07-10

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06F15/16 ,  G06Q50/00

    CPC分类号： G06Q50/01

    摘要： A user's connections in a social networking system are grouped into a number of clusters based on a measure of the connections' relationships, or affinity, to each other. The affinities among the connections are based on the connections' own relationships and indicate a likelihood that the connections are in the same social circles. The clusters are formed based on the affinities among the user's connections, where the clusters tend to have connections that have relatively high affinities with the other connections the same cluster as compared to the connections who are not in the same cluster. An iterative hierarchical clustering algorithm may be used to collapse the connections into clusters based on affinities between pairs of the connections.

    TARGETING ADVERTISEMENTS IN A SOCIAL NETWORK
    6.
    发明申请
    TARGETING ADVERTISEMENTS IN A SOCIAL NETWORK 审中-公开
    在社会网络中指导广告

    公开(公告)号：US20090070219A1

    公开(公告)日：2009-03-12

    申请号：US12195321

    申请日：2008-08-20

    申请人： Adam D'Angelo ,  Aditya Agarwal ,  Kang-Xing Jin ,  Yun-Fang Juan ,  Levy Klots ,  Oleksandr Moskalyuk ,  Yishan Wong

    发明人： Adam D'Angelo ,  Aditya Agarwal ,  Kang-Xing Jin ,  Yun-Fang Juan ,  Levy Klots ,  Oleksandr Moskalyuk ,  Yishan Wong

    IPC分类号： G06Q30/00 ,  G06Q10/00 ,  G06Q40/00 ,  G06Q90/00

    CPC分类号： H04L67/306 ,  G06Q10/10 ,  G06Q30/02 ,  G06Q30/0247 ,  G06Q30/0251 ,  G06Q30/0258 ,  G06Q30/0269 ,  G06Q40/04 ,  H04L67/18 ,  H04L67/22 ,  H04W4/029

    摘要： A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website presents targeted ads based on actions by the member and one or more characteristics of the member. The social networking website maintains a profile associated with the member which describes characteristics of the member, such as age, geographic location, employment, educational history and interests. The social networking website compares the member profile to targeting criteria for a plurality of advertising requests and determines the advertising requests that match the member profile and generate the most revenue for the social networking website. When presenting a member with an ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the ad.

    摘要翻译： 社交网站记录有关网站成员采取的行动的信息。 对于网站的特定成员，网站根据会员的行为和会员的一个或多个特征提供有针对性的广告。 社交网站维护与成员有关的简介，描述会员的特征，如年龄，地理位置，就业，教育历史和兴趣。 社交网站将成员简档与多个广告请求的定位标准进行比较，并确定与成员简档匹配的广告请求，并为社交网站创造最多的收入。 在向成员展示广告时，网站可以通过从接收到的广告中选择一个可以最大化广告预期价值的广告来优化广告收入。

    Clustering a User's Connections in a Social Networking System
    9.
    发明申请
    Clustering a User's Connections in a Social Networking System 有权
    在社交网络系统中聚合用户连接

    公开(公告)号：US20130013682A1

    公开(公告)日：2013-01-10

    申请号：US13179547

    申请日：2011-07-10

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06F15/16

    CPC分类号： G06Q50/01

    摘要： A user's connections in a social networking system are grouped into a number of clusters based on a measure of the connections' relationships, or affinity, to each other. The affinities among the connections are based on the connections' own relationships and indicate a likelihood that the connections are in the same social circles. The clusters are formed based on the affinities among the user's connections, where the clusters tend to have connections that have relatively high affinities with the other connections the same cluster as compared to the connections who are not in the same cluster. An iterative hierarchical clustering algorithm may be used to collapse the connections into clusters based on affinities between pairs of the connections.

    摘要翻译： 基于对彼此的连接关系或亲和度的度量，社交网络系统中的用户的连接被分组为多个聚类。 连接之间的亲和力基于连接自身的关系，并指出连接在同一个社交圈中的可能性。 基于用户连接之间的亲和度形成集群，其中集群倾向于具有与不在同一集群中的连接相比具有相同集群的其他连接具有相对高亲和度的连接。 可以使用迭代层次聚类算法基于连接对之间的亲和度将连接折叠成簇。

    Top Friend Prediction for Users in a Social Networking System
    10.
    发明申请
    Top Friend Prediction for Users in a Social Networking System 审中-公开
    社交网络系统中用户的热门朋友预测

    公开(公告)号：US20120271722A1

    公开(公告)日：2012-10-25

    申请号：US13093744

    申请日：2011-04-25

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06Q30/02 ,  G06F15/18

    CPC分类号： G06Q10/04

    摘要： A social networking system predicts a user's top friends among the user's connections in a social networking system. A top friend prediction model receives static data and statistics related to the historical interactions of the connection and the user as input singles. The model may be trained using a training set of data associated with the connections of users, where users have explicitly indicated that other users are or are not their top (or “best” or “closest”) friends. The trained model outputs a score for each of a particular user's connections, and the score is used to predict whether the connection is a top friend of that user. Whether a user's connection is one of that user's top friends thus indicates a closeness of that relationship in the real world, which may differ from how likely the users are to interact with each other within the social networking system.

    摘要翻译： 社交网络系统预测用户在社交网络系统中的用户连接中的最佳朋友。 顶级朋友预测模型接收与连接和用户的历史相互作用相关的静态数据和统计信息作为输入单个。 可以使用与用户的连接相关联的数据的训练集来训练该模型，其中用户明确地指出其他用户是或不是他们的顶部（或最佳或最接近）的朋友。 经过训练的模型为每个特定用户的连接输出分数，并且分数用于预测该连接是否是该用户的最佳朋友。 用户的连接是否是该用户的顶级朋友之一，因此表明该关系在现实世界中的接近度，这可能与用户在社交网络系统内彼此交互的可能性有差异。