公开(公告)号：US09805378B1

公开(公告)日：2017-10-31

申请号：US13631084

申请日：2012-09-28

Applicant: Google Inc.

Inventor： Li Wei ,  James Vincent McFadden

IPC: G06Q30/02 ,  H04N21/25

CPC classification number: G06Q30/02 ,  H04N21/251 ,  H04N21/26233 ,  H04N21/4826

Abstract: A system receives a user request for a media item and identifies candidate media items for suggesting to the user with the media item. The system predicts a user consumption time for each candidate media item and selects a sub-set of the candidate media items that have higher predicted user consumption times. The system provides the requested media item with the sub-set of the candidate media items.

公开(公告)号：US09390382B2

公开(公告)日：2016-07-12

申请号：US14142970

申请日：2013-12-30

Applicant: Google Inc.

Inventor： Yoram Singer ,  Tal Shaked ,  Tushar Deepak Chandra ,  Tze Way Eugene Ie ,  James Vincent McFadden ,  Jeremiah Harmsen ,  Kristen Riedt LeFevre

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.

Abstract translation: 公开了用于基于与一个或多个特征相关联的一个或多个正则化惩罚来训练机器学习模型的系统和技术。 具有较低正则化罚分的模板可以优先于具有较高正则化惩罚的模板。 正规化惩罚可以根据领域知识来确定。 基于确定模板出现低于稳定性阈值，可以将限制性正则化惩罚分配给模板，并且如果模板发生满足或超过稳定性阈值，则可以对模板进行修改。

公开(公告)号：US09552555B1

公开(公告)日：2017-01-24

申请号：US14816866

申请日：2015-08-03

Applicant: Google Inc.

Inventor： Yangli Hector Yee ,  James Vincent McFadden ,  John Kraemer ,  Dasarathi Sampath

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06N99/005 ,  G06F17/3053 ,  G06F17/30554 ,  G06F17/30699 ,  G06F17/30781 ,  G06F17/30864 ,  G06N3/0472 ,  G06N7/005

Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.

Abstract translation: 提供了基于主题推荐内容的机制。 在一些实现中，提供了一种用于推荐内容项的方法，包括：确定与用户相关联的多个被访问的内容项，其中所述多个内容项中的每一个与多个主题相关联; 确定与所述多个所访问的内容项中的每一个相关联的所述多个主题; 基于所述多个主题生成用户兴趣的模型，其中所述模型实现机器学习技术以确定用于分配到所述多个主题中的每一个的多个权重; 对于多个内容项目，应用所述模型来确定所述用户将观看所述多个内容项目中的内容项目的概率; 基于所确定的概率对多个内容项进行排序; 以及基于所述排列的内容项目来选择所述多个内容项目的子集以推荐给所述用户。

公开(公告)号：US20150186794A1

公开(公告)日：2015-07-02

申请号：US14142970

申请日：2013-12-30

Applicant: Google Inc.

Inventor： Yoram Singer ,  Tal Shaked ,  Tushar Deepak Chandra ,  Tze Way Eugene Ie ,  James Vincent McFadden ,  Jeremiah Harmsen ,  Kristen Riedt LeFevre

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.

Abstract translation: 公开了用于基于与一个或多个特征相关联的一个或多个正则化惩罚来训练机器学习模型的系统和技术。 具有较低正则化罚分的模板可以优先于具有较高正则化惩罚的模板。 正规化惩罚可以根据领域知识来确定。 基于确定模板出现低于稳定性阈值，可以将限制性正则化惩罚分配给模板，并且如果模板发生满足或超过稳定性阈值，则可以对模板进行修改。

公开(公告)号：US20200151614A1

公开(公告)日：2020-05-14

申请号：US14106900

申请日：2013-12-16

Applicant: Google Inc.

Inventor： Tal Shaked ,  Tushar Deepak Chandra ,  James Vincent McFadden ,  Yoram Singer ,  Tze Way Eugene Ie

IPC: G06N99/00

Abstract: Systems and techniques are provided for template exploration in a large-scale machine learning system. A method may include obtaining multiple base templates, each base template comprising multiple features. A template performance score may be obtained for each base template and a first base template may be selected from among the multiple base templates based on the template performance score of the first base template. Multiple cross-templates may be constructed by generating a cross-template of the selected first base template and each of the multiple base templates. Performance of a machine learning model may be tested based on each cross-template to generate a cross-template performance score for each of the cross-templates. A first cross-template may be selected from among the multiple cross-templates based on the cross-template performance score of the cross-template. Accordingly, the first cross-template may be added to the machine learning model.

公开(公告)号：US20170103343A1

公开(公告)日：2017-04-13

申请号：US15384692

申请日：2016-12-20

Applicant: Google Inc.

Inventor： Yangli Hector Yee ,  James Vincent McFadden ,  John Kraemer ,  Dasarathi Sampath

IPC: G06N99/00 ,  G06F17/30 ,  G06N7/00

CPC classification number: G06N20/00 ,  G06F16/24578 ,  G06F16/248 ,  G06F16/335 ,  G06F16/70 ,  G06F16/951 ,  G06N3/0472 ,  G06N7/005

Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.