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    • 66. 发明申请
    • CONTENT RECOMMENDATIONS
    • 内容建议
    • US20150339381A1
    • 2015-11-26
    • US14284647
    • 2014-05-22
    • Yahoo!, Inc.
    • Vidit JainAbhranil Chatterjee
    • G06F17/30
    • G06F17/30958
    • Users consume a wide variety of content from various sources, such as videos accessible through websites. As provided herein, content recommendations that are contextually and/or semantically relevant to current content consumed by a user may be identified and provided to the user. For example, metadata for a video being watched by the user may be identified (e.g., terms extracted from a description, user reviews, a category, and/or other information). The metadata may be used to identify content recommendations based upon the metadata corresponding to terms grouped into a set of refined topic groupings of a graph comprising terms and relationships between terms extracted from a content corpus. The metadata may be matched to relevant terms within the set of refined topic groupings, and content recommendations comprising content corresponding to the relevant terms may be suggested to the user.
    • 用户可以从各种来源(例如通过网站访问的视频)消费各种各样的内容。 如本文所提供的,可以识别与用户所消费的当前内容上下文和/或语义相关的内容推荐,并将其提供给用户。 例如,可以识别由用户观看的视频的元数据(例如,从描述提取的术语,用户评论,类别和/或其他信息)。 元数据可以用于基于对应于分组为包括从内容语料库提取的术语和关系之间的关系的图的精简主题分组的集合的元数据来标识内容建议。 元数据可以与精简主题分组集合内的相关项匹配,并且可以向用户建议包括对应于相关术语的内容的内容建议。
    • 67. 发明授权
    • Tokenization platform
    • 令牌平台
    • US09195738B2
    • 2015-11-24
    • US13572825
    • 2012-08-13
    • Jignashu Parikh
    • Jignashu Parikh
    • G06F17/27G06F17/30
    • G06F17/30684G06F17/2735G06F17/277
    • A tokenization platform and method is described for accurately tokenizing character strings, including but not limited to non-delimited character strings of the type commonly used in Internet domain names and computer filenames, to accurately identify words and phrases occurring therein. In one embodiment, a phased tokenization approach is used in which the final phase is a lexical analysis-based tokenization using a dictionary. The dictionary may be advantageously created and updated based upon one or more query logs associated with respective information retrieval systems, thereby ensuring that the dictionary accurately reflects currently-used terminology and captures alternative spellings and presentations of words and phrases submitted by users.
    • 描述了用于准确地标记字符串的标记平台和方法,包括但不限于在因特网域名和计算机文件名中常用的类型的非分隔字符串,以准确地识别其中出现的字和短语。 在一个实施例中,使用相位令牌化方法,其中最后阶段是使用词典的基于词汇分析的令牌化。 可以基于与相应信息检索系统相关联的一个或多个查询日志来有利地创建和更新字典,从而确保字典准确地反映当前使用的术语,并且捕获由用户提交的单词和短语的备选拼写和呈现。
    • 70. 发明授权
    • Hot within my communities
    • 在我的社区热
    • US09178951B2
    • 2015-11-03
    • US13615077
    • 2012-09-13
    • Todd SampsonJohn SampsonSteve HoEric MarcoullierNeil Scott Rafer
    • Todd SampsonJohn SampsonSteve HoEric MarcoullierNeil Scott Rafer
    • G06F15/16H04L29/08G06Q10/10
    • H04L67/22G06Q10/10
    • Embodiments of the invention are directed to identifying network resources or other topics that are of interest to members of multiple online communities to which a user belongs. Online communities include blogs, websites, games, e-commerce systems, messaging systems, wikis, etc. For each online community, click activity or other client behaviors are tracked and analyzed to determine statistical metrics about community activity, such as which articles, links, services, or other network resources are popular in the online community. At least some of the tracking or analysis can be performed by clients that access the online communities, by a server of each online community, and/or by a central tracking system. The results for each community may be further analyzed relative to each other. The results are provided for all communities with which a given user is associated. For example, a list of the most popular links in the user's selected online communities.
    • 本发明的实施例涉及识别用户所属的多个在线社区的成员感兴趣的网络资源或其他主题。 在线社区包括博客,网站,游戏,电子商务系统,邮件系统,维基等。对于每个在线社区,点击活动或其他客户行为进行跟踪和分析,以确定关于社区活动的统计指标,例如哪些文章,链接 ,服务或其他网络资源在在线社区中很受欢迎。 至少一些跟踪或分析可以由访问在线社区的客户端,每个在线社区的服务器和/或中央跟踪系统执行。 每个社区的结果可以相对于彼此进一步分析。 为与给定用户关联的所有社区提供结果。 例如,用户选择的在线社区中最受欢迎的链接列表。