中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

4 records (took 0.012 seconds)

    Content Recommendation System using a Neural Network Language Model
    1.
    发明申请
    Content Recommendation System using a Neural Network Language Model 有权
    使用神经网络语言模型的内容推荐系统

    公开(公告)号：US20150178265A1

    公开(公告)日：2015-06-25

    申请号：US14136111

    申请日：2013-12-20

    Applicant: Google Inc.

    Inventor： Glen Anderson ,  Michael Schuster

    IPC: G06F17/27 ,  G06F17/28

    CPC classification number: G06Q30/0255 ,  G06F17/276 ,  G06F17/277 ,  G06F17/28 ,  G06F17/3064 ,  G06F17/30864 ,  G06F17/30873 ,  G06N3/02 ,  G06N3/08 ,  G06Q30/0631 ,  H04N21/44222 ,  H04N21/4666 ,  H04N21/4668

    Abstract: The present disclosure relates to applying techniques similar to those used in neural network language modeling systems to a content recommendation system. For example, by associating consumed media content to words of a language model, the system may provide content predictions based on an ordering. Thus, the systems and techniques described herein may produce enhanced prediction results for recommending content (e.g. word) in a given sequence of consumed content. In addition, the system may account for additional user actions by representing particular actions as punctuation in the language model.

    Abstract translation: 本公开涉及将类似于神经网络语言建模系统中使用的技术应用于内容推荐系统。 例如，通过将消耗的媒体内容与语言模型的单词相关联，系统可以基于排序来提供内容预测。 因此，本文描述的系统和技术可以产生用于在给定的消费内容序列中推荐内容（例如，单词）的增强的预测结果。 此外，系统可以通过将特定动作表示为语言模型中的标点符号来解释额外的用户操作。

    IMPLICIT BRIDGING OF MACHINE LEARNING TASKS
    2.
    发明申请
    IMPLICIT BRIDGING OF MACHINE LEARNING TASKS 审中-公开

    公开(公告)号：US20180129972A1

    公开(公告)日：2018-05-10

    申请号：US15394708

    申请日：2016-12-29

    Applicant: Google Inc.

    Inventor： Zhifeng Chen ,  Michael Schuster ,  Melvin Jose Johnson Premkumar ,  Yonghui Wu ,  Quoc V. Le ,  Maxim Krikun ,  Thorsten Brants

    IPC: G06N99/00 ,  G06F17/28

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to process the augmented model input to generate a machine learning model output of the first type for the model input.

    Content recommendation system using a neural network language model
    3.
    发明授权
    Content recommendation system using a neural network language model 有权
    内容推荐系统采用神经网络语言模型

    公开(公告)号：US09535897B2

    公开(公告)日：2017-01-03

    申请号：US14136111

    申请日：2013-12-20

    Applicant: Google Inc.

    Inventor： Glen Anderson ,  Michael Schuster

    IPC: G06F17/27 ,  G06F17/28 ,  G06N3/02 ,  G06Q30/06 ,  H04N21/466 ,  G06F17/30 ,  H04N21/442

    CPC classification number: G06Q30/0255 ,  G06F17/276 ,  G06F17/277 ,  G06F17/28 ,  G06F17/3064 ,  G06F17/30864 ,  G06F17/30873 ,  G06N3/02 ,  G06N3/08 ,  G06Q30/0631 ,  H04N21/44222 ,  H04N21/4666 ,  H04N21/4668

    Abstract: The present disclosure relates to applying techniques similar to those used in neural network language modeling systems to a content recommendation system. For example, by associating consumed media content to words of a language model, the system may provide content predictions based on an ordering. Thus, the systems and techniques described herein may produce enhanced prediction results for recommending content (e.g. word) in a given sequence of consumed content. In addition, the system may account for additional user actions by representing particular actions as punctuation in the language model.

    Abstract translation: 本公开涉及将类似于神经网络语言建模系统中使用的技术应用于内容推荐系统。 例如，通过将消耗的媒体内容与语言模型的单词相关联，系统可以基于排序来提供内容预测。 因此，本文描述的系统和技术可以产生用于在给定的消费内容序列中推荐内容（例如，单词）的增强的预测结果。 此外，系统可以通过将特定动作表示为语言模型中的标点符号来解释额外的用户操作。

Patent Agency Ranking