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    • 1. 发明授权
    • Using clicked slate driven click-through rate estimates in sponsored search
    • 在赞助搜索中使用点击式平板驱动的点击率估算
    • US08364525B2
    • 2013-01-29
    • US12956496
    • 2010-11-30
    • Divy KothiwalKannan AchanEren ManavogluErick Cantu-Paz
    • Divy KothiwalKannan AchanEren ManavogluErick Cantu-Paz
    • G06Q30/00
    • G06Q30/0256G06Q30/0241G06Q30/0251
    • A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically). A system for implementing the method includes aggregating data from a historical database using selectors such as a position selector, a click feature selector, an impression-advertiser-campaign-creative selector, and a commercial intent selector.
    • 一种计算机实现的方法和系统,用于使用用于从历史数据库确定意图驱动的点击的技术,基于用户的商业意图(涉及主题广告)在赞助搜索系统中选择主题广告。 该方法包括用于聚合训练模型数据集的步骤,其中训练模型数据集包含所选择的点击历史。 然后,从训练模型数据集中选择点击的图表(进一步选择点击次数),点击的图片包括一组点击的广告,以及计算该主题广告的意图驱动的点击反馈值。 该方法包括使用与在特定时间段(静态或动态确定的时间段)内接收的点击对应的特征来选择点击的平板的技术。 用于实现该方法的系统包括使用诸如位置选择器,点击特征选择器,展示广告商 - 广告系列创意选择器和商业意图选择器之类的选择器来汇总来自历史数据库的数据。
    • 2. 发明申请
    • Ad Relevance In Sponsored Search
    • 广告相关性在赞助搜索
    • US20110270672A1
    • 2011-11-03
    • US12769446
    • 2010-04-28
    • Dustin HillardHema RaghavanEren ManavogluChris LeggetterStefan Schroedl
    • Dustin HillardHema RaghavanEren ManavogluChris LeggetterStefan Schroedl
    • G06Q30/00G06F15/18
    • G06Q30/02G06Q30/0243G06Q30/0246
    • Techniques for improving advertisement relevance for sponsored search advertising. The method includes steps for processing a click history data structure containing at least a plurality of query-advertisement pairs, populating a first translation table containing a co-occurrence count field, populating a second translation table containing an expected clicks field, and calculating a click propensity score for an advertisement using the click history data structure, the first translation table (for determining overall click likelihood across all historical traffic), and using the second translation table (for removing biases present in the first translation table). Other method steps calculate a second click propensity score for a second advertisement, then ranking the first advertisement relative to the second advertisement for comparing a click propensity score to a threshold for filtering low quality ad candidates from a plurality of ad candidates, and then ranking advertisements for optimizing placement of ads on a sponsored search display page.
    • 用于提高赞助搜索广告广告相关性的技术。 该方法包括处理包含至少多个查询 - 广告对的点击历史数据结构的步骤,填充包含同现计数字段的第一翻译表,填充包含预期点击字段的第二翻译表,以及计算点击 用于使用点击历史数据结构的广告的倾向得分,第一翻译表(用于确定所有历史流量中的整体点击可能性)以及使用第二转换表(用于去除存在于第一翻译表中的偏差)。 其他方法步骤计算第二广告的第二点击倾向得分,然后相对于第二广告对第一广告进行排名,用于将点击倾向得分与用于从多个广告候选中过滤低质量广告候选的阈值进行比较,然后排列广告 用于优化广告在赞助的搜索显示页面上的展示位置。
    • 6. 发明申请
    • SYSTEM AND METHOD FOR PREDICTING CONTEXT-DEPENDENT TERM IMPORTANCE OF SEARCH QUERIES
    • 用于预测搜索查询的背景相关重要性的系统和方法
    • US20110131157A1
    • 2011-06-02
    • US12626892
    • 2009-11-28
    • Rukmini IyerEren ManavogluHema Raghavan
    • Rukmini IyerEren ManavogluHema Raghavan
    • G06F17/30G06F15/18
    • G06Q30/0251
    • An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.
    • 提供了一种用于识别查询的上下文相关项重要性的改进的系统和方法。 使用对查询的上下文相关项重要性的监督学习来学习查询词重要性模型,然后将其用作查询词语的重要度权重作为查询特征应用于广告预测。 例如,用于查询重写的查询项重要性模型可以预测与查询匹配的重写查询与被分配为查询特征的术语重要性权重。 或者用于广告预测的查询词重要性模型可以预测具有被指定为查询特征的术语重要性权重的查询的相关广告。 在一个实施例中,赞助的广告选择引擎选择由查询词语重要性引擎评分的赞助广告,该查询词语重要性引擎使用术语重要性权重作为查询特征和逆文档频率权重作为广告特征来分配相关性得分。
    • 8. 发明授权
    • Using linear and log-linear model combinations for estimating probabilities of events
    • 使用线性和对数线性模型组合来估计事件的概率
    • US08484077B2
    • 2013-07-09
    • US12893939
    • 2010-09-29
    • Ozgur CetinEren ManavogluKannan AchanErick Cantu-PazRukmini Iyer
    • Ozgur CetinEren ManavogluKannan AchanErick Cantu-PazRukmini Iyer
    • G06Q30/00
    • G06Q30/0277G06Q10/04G06Q30/0241
    • A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.
    • 一种将在线广告系统中的点击模型的多种概率组合成组合预测模型的方法,该方法通过接收特征集切片(例如,对应于人口统计学或分类或群集)开始,并且使用分片数据来训练多个切片 - 明智的预测模型。 训练的切片预测模型通过在训练的切片预测模型上重叠加权分布模型来组合。 然后,组合预测模型用于预测在线广告中给予查询广告对的点击的概率。 该方法可以灵活地接收切片规格,并且可以覆盖各种分布模型中的任何一个或多个,例如线性组合或对数线性组合。 使用适当的加权分布模型,组合预测模型可靠地产生至少与切片预测模型集中的最佳预测模型一样好的点击事件发生的预测估计。