Amazon Technologies, Inc.

Charles Lawrence Scott

Daniel Bruce Lloyd

Brett Patrick Canfield

Seong Jin Park

Soo-Min Pantel

Sungro Yoon

Roshan Harish Makhijani

Pranav Varia

Alex Michael Shipman

Corey Lucius

Byung Ju Lee

Sneha Rajana

Ben Allison

Xiaonan Zhao

Jack Lightbody

Zheng Zheng Xing

Paul Phillip Mannino

Systems and methods are provided for rendering a first interface for presentation by a user computing device based on a first user input and information received over a network. The first interface can include item information. Additional item information may be stored at the user computing device at the time that the first interface is processed, but not initially displayed. Based on receiving a second user input, a second interface can be displayed that includes the additional item information, where the second interface may be displayed over a first portion of the first interface such that the first user interface remains accessible. The second interface may be populated with portions of the previously stored additional item information without necessarily obtaining further information over the network.

Caching item information for display in an interface overlay

Disclosed are one or more embodiments for a unique and personalized experience for a user interacting with an electronic commerce site by identifying user-preferred item attributes using supervised machine learning and presenting items to the user in an arrangement that is based on the identified item attributes. A shopping mission is determined according to user interactions with an electronic commerce site. The shopping mission is applied to an attribute prediction model that is trained to detect user-preferred item attributes for items included the item category and estimate a likelihood that an item containing a particular attribute will be purchased or interacted with during the interactions with the electronic commerce site.

User-preferred item attributes

Systems and methods are described for analyzing one or more online shopping missions to determine an estimated stage of a user within the mission and to adjust content for display to the user based on the estimated stage of the user within the mission. The estimated stage of the user within the mission may be determined using a model, such as a machine learning model, that accepts as input signals corresponding to the online shopping mission. The signals may represent user interface interactions and/or other data relating to the user and the mission.

Modification of presented content based on historical data in a shopping mission

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Seattle, WA, United States of America

Charles Lawrence Scott