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    • 11. 发明授权
    • Method for selecting electronic advertisements using machine translation techniques
    • 使用机器翻译技术选择电子广告的方法
    • US08145649B2
    • 2012-03-27
    • US12970757
    • 2010-12-16
    • Vanessa MurdockMassimiliano CiaramitaVassilis Plachouras
    • Vanessa MurdockMassimiliano CiaramitaVassilis Plachouras
    • G06F7/00G06F17/30
    • G06Q30/02
    • A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.
    • 公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。 为了选择广告,系统采取利用机器翻译技术的内容匹配的方法。 本发明的系统通过简单有效的机器翻译功能来实现这一目标,这些机器翻译功能是从周围环境中提取的,以与潜在的广告池相匹配。 机器翻译功能用作训练机器学习模型的特征。 在一个实施例中,经训练以识别与特定上下文相关的广告的排名SVM(支持向量机)。 训练后的机器学习模型然后可以用于通过向机器学习模型提供用于广告和周围环境的机器翻译特征措施来对特定上下文的广告进行排名。
    • 12. 发明授权
    • Method for matching electronic advertisements to surrounding context based on their advertisement content
    • 基于其广告内容将电子广告与周围环境进行匹配的方法
    • US08073803B2
    • 2011-12-06
    • US11778540
    • 2007-07-16
    • Vanessa MurdockVassilis PlachourasMassimiliano Ciaramita
    • Vanessa MurdockVassilis PlachourasMassimiliano Ciaramita
    • G06F17/00G06N5/02
    • G06Q30/02G06Q30/0276
    • A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.
    • 公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。 为了选择广告,系统采取内容匹配的方法,重点是捕获周围内容和广告内容之间的细微的语言关联。 本发明的系统通过简单而有效的语义关联度量来实现这一目标,这些措施涉及诸如“大哥”或“强茶”等常规多字表达的词汇搭配。 语义关联度量被用作训练机器学习模型的特征。 在一个实施例中,经训练以识别与特定上下文相关的广告的排名SVM(支持向量机)。 训练后的机器学习模型然后可以用于通过向机器学习模型提供广告和周围环境的语义关联度量来对特定上下文的广告进行排名。
    • 13. 发明申请
    • Method for Selecting Electronic Advertisements Using Machine Translation Techniques
    • 使用机器翻译技术选择电子广告的方法
    • US20110087680A1
    • 2011-04-14
    • US12970757
    • 2010-12-16
    • Vanessa MurdockMassimiliano CiaramitaVassilis Plachouras
    • Vanessa MurdockMassimiliano CiaramitaVassilis Plachouras
    • G06F7/00G06F17/30G06F17/28
    • G06Q30/02
    • A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.
    • 公开了一种用于从广告池中选择电子广告以匹配周围内容的系统。 为了选择广告,系统采取利用机器翻译技术的内容匹配的方法。 本发明的系统通过简单有效的机器翻译功能来实现这一目标,这些机器翻译功能是从周围环境中提取的,以与潜在的广告池相匹配。 机器翻译功能用作训练机器学习模型的特征。 在一个实施例中,经训练以识别与特定上下文相关的广告的排名SVM(支持向量机)。 训练后的机器学习模型然后可以用于通过向机器学习模型提供用于广告和周围环境的机器翻译特征措施来对特定上下文的广告进行排名。
    • 20. 发明授权
    • System and methodology for a multi-site search engine
    • 多站点搜索引擎的系统和方法
    • US08095545B2
    • 2012-01-10
    • US12250929
    • 2008-10-14
    • Luca TelloliFlavio JunqueriaAristides GionisVassilis PlachourasRicardo Baeza-Yates
    • Luca TelloliFlavio JunqueriaAristides GionisVassilis PlachourasRicardo Baeza-Yates
    • G06F7/02
    • G06F17/30864
    • Techniques for query processing in a multi-site search engine are described. During an indexing phase, each site of a multi-site search engine indexes a set of assigned web resources and each site calculates, for each term in the set of assigned web resources, a site-specific upper bound ranking score on the contribution of the term to the search engine ranking function for a query containing the term. During a propagation phase, all sites exchange their site-specific upper bound ranking scores with each other. In response to a site receiving a query, the site determines the set of locally matching resources and compares the ranking score of a locally matching resource with the site-specific upper bound ranking scores for the terms of the query that were received during the propagation phase and determines whether to communicate the query to other sites. By exchanging appropriately defined site-specific upper bound ranking scores, the site initially receiving the query can determine whether the locally matching resources would be identical to the resources obtained from a single-site search system without having to communicate the query to each of the other sites.
    • 描述了在多站点搜索引擎中查询处理的技术。 在索引阶段期间,多站点搜索引擎的每个站点对一组分配的web资源进行索引,并且每个站点针对所分配的web资源集合中的每个术语来计算一个站点特定的上限排名分数 用于包含该术语的查询的搜索引擎排名函数。 在传播阶段,所有的站点彼此交换其站点特定的上限排名得分。 响应于接收到查询的站点,站点确定本地匹配资源的集合,并将本地匹配资源的排名得分与在传播阶段期间接收到的查询的项的站点特定上限排名得分进行比较 并确定是否将查询传递给其他站点。 通过交换适当定义的站点特定上限排名得分,最初接收查询的站点可以确定本地匹配资源是否与从单站点搜索系统获得的资源相同,而不必将查询传递给其他每个 网站。