Amazon Technologies, Inc.

Nikolaos Chatzipanagiotis

Pragyana K Mishra

Roopesh Ranjan

Narayanan Sadagopan

Can Cui

Anil Padia

Nikhil Raina

Tamal Krishna Kuila

Shikha Aggarwal

Shivani Matta

Aspects of the present disclosure relate to machine learning techniques for identifying the incremental impact of different past events on the likelihood that a target outcome will occur. The technology can use a recurrent neural network to analyze two different representations of an event sequence&#x2014;one in which some particular event occurs, and another in which that particular event does not occur. The incremental impact of that particular event can be determined based on the calculated difference between the probabilities of the target outcome occurring after these two sequences.

Machine learning analysis of incremental event causality towards a target outcome

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.

Causal analysis system

A prediction model may be created to predict future actions likely to be performed by users while interacting with electronic content via user devices. The predictions may be used to streamline access to interface controls or other information to enable the users to facilitate or expedite performance of the predicted actions, while reducing computational demands on computing devices that provide the electronic content by, for example, reducing unnecessary intervening computing actions.

Predictive action modeling to streamline user interface

Systems, methods, and computer-readable media are disclosed for systems and methods of determining quantitative values representative of user action automaticity. Example methods may include determining a first request for a first user interface from a user device, determining a user identifier associated with the first request, and determining user interaction history data using the user identifier. Example methods may include determining a first selectable option for presentation in a first position at the first user interface using the user interaction history, determining a second selectable option for presentation in a second position at the first user interface, generating the first user interface, and sending the first user interface to the user device.

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Seattle, WA, United States of America

Nikolaos Chatzipanagiotis