公开(公告)号：US11468348B1

公开(公告)日：2022-10-11

申请号：US16788100

申请日：2020-02-11

Applicant: Amazon Technologies, Inc.

Inventor： Can Cui ,  Nikolaos Chatzipanagiotis ,  Tamal Krishna Kuila ,  Narayanan Sadagopan

IPC: G06N20/00 ,  G06N5/04 ,  G06K9/62 ,  G06N3/04

Abstract: Methods and apparatus for identifying features that may have a high potential impact on key application metrics. These methods rely on observational data to estimate the importance of application features, and use causal inference tools such as Double Machine Learning (double ML) or Recurrent Neural Networks (RNN) to estimate the impacts of treatment features on key metrics. These methods may allow developers to estimate the effectiveness of features without running online experiments. These methods may, for example, be used to effectively plan and prioritize online experiments. Results of the online experiments may be used to optimize key metrics of mobile applications, web applications, websites, and other web-based programs.

公开(公告)号：US10552863B1

公开(公告)日：2020-02-04

申请号：US15353665

申请日：2016-11-16

Applicant: Amazon Technologies, Inc.

Inventor： Narayanan Sadagopan ,  Neela Kamlakar Sawant

IPC: G06Q30/02 ,  G06N20/00

Abstract: Systems and methods are provided for optimizing campaigns (such as marketing campaigns) based on both short term and long term behaviors of users. A computing system learns an incremental outcome prediction model using training data comprising a marketing campaign log entry of a subject user and another marketing campaign log entry of a corresponding user, which represents a counterfactual outcome for the subject user. A marketing campaign can be selected for another user using the learned model.