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    • 31. 发明申请
    • PREDICTING KEYWORD MONETIZATION
    • 预测关键词制衡
    • US20090299855A1
    • 2009-12-03
    • US12131125
    • 2008-06-02
    • Hua LiZheng ChenJian Wang
    • Hua LiZheng ChenJian Wang
    • G06Q30/00
    • G06Q30/08G06Q30/02G06Q30/0256G06Q30/0267
    • Embodiments of the claimed subject matter provide a method and system for predicting bidding keyword monetization. The claimed subject matter provides a method and system with which the value of a keyword for the purpose of relevant online advertisement may be evaluated according to various metrics to determine a bidding landscape for use in advertising campaigns. The value of the keyword considers certain attributes related to the monetization of the keyword.One embodiment of the claimed subject matter is implemented as a method for predicting keyword monetization for one or more keyword-advertisement relationships. Historical data for the one or more keyword-advertisement relationships is referenced and used to generate a global model of the one or more keyword-advertisement relationship. The relationships are then evaluated according to a time-series analysis, which parses the data from the historical data and the global model to create predictions for the keyword monetization according to the keyword-advertisement relationships.
    • 所要求保护的主题的实施例提供了用于预测投标关键字货币化的方法和系统。 所要求保护的主题提供了一种方法和系统,其中可以根据各种度量来评估用于相关在线广告的关键字的价值,以确定用于广告活动的投标景观。 该关键字的值考虑与关键字获利相关的特定属性。 所要求保护的主题的一个实施例被实现为用于预测一个或多个关键字 - 广告关系的关键字获利的方法。 引用一个或多个关键字 - 广告关系的历史数据,并用于生成一个或多个关键字 - 广告关系的全局模型。 然后根据时间序列分析来评估关系,该时间序列分析从历史数据和全球模型中分析数据,以根据关键字 - 广告关系创建关键字营利的预测。
    • 33. 发明授权
    • Smart attribute classification (SAC) for online reviews
    • 智能属性分类(SAC)用于在线评论
    • US08682896B2
    • 2014-03-25
    • US13412871
    • 2012-03-06
    • Jian HuJian-Tao SunZheng Chen
    • Jian HuJian-Tao SunZheng Chen
    • G06F7/00G06F17/30
    • G06N99/005G06F17/30707
    • Techniques for identifying attributes in a sentence and determining a number of attributes to be associated with the sentence is described. An attribute identification (AI) framework comprises an offline training portion, an online prediction portion, and an AI algorithm module. The offline training portion utilizes the relationships between attributes within sentences input to the offline training portion to improve attribute identification of the AI algorithm module. The online prediction portion predicts, for each sentence input, the attributes of the sentence and the number of attributes the sentence is associated with by employing the AI algorithm module.
    • 描述用于识别句子中的属性并确定与句子相关联的属性的数量的技术。 属性识别(AI)框架包括离线训练部分,在线预测部分和AI算法模块。 离线训练部分利用输入到离线训练部分的句子内的属性之间的关系来改善AI算法模块的属性识别。 在线预测部分通过使用AI算法模块来预测每个句子输入的句子的属性和句子相关联的属性的数量。
    • 34. 发明授权
    • Smart attribute classification (SAC) for online reviews
    • 智能属性分类(SAC)用于在线评论
    • US08156119B2
    • 2012-04-10
    • US12355987
    • 2009-01-19
    • Jian HuJian-Tao SunZheng Chen
    • Jian HuJian-Tao SunZheng Chen
    • G06F7/00G06F17/30
    • G06N99/005G06F17/30707
    • Techniques for identifying attributes in a sentence and determining a number of attributes to be associated with the sentence are described. The techniques employ an offline training portion, an online prediction portion, and an attribute identification algorithm. The offline training portion utilizes relationships between attributes within sentences input to the offline training portion to improve attribute identification of the attribute identification algorithm. The online prediction portion predicts, for each sentence input, the attributes of the sentence, and the number of attributes the sentence is associated with by employing the attribute identification algorithm.
    • 描述用于识别句子中的属性并确定与句子相关联的属性的数量的技术。 该技术采用离线训练部分,在线预测部分和属性识别算法。 离线训练部分利用输入到离线训练部分的句子内的属性之间的关系来改进属性识别算法的属性识别。 在线预测部分通过使用属性识别算法来预测每个句子输入的句子的属性以及该句子所关联的属性的数量。