Amazon Technologies, Inc.

Stephen Brent Ivie

David Michael Hurley

Aaron James Dykstra

Adam James Finkelstein

Saurabh Nangia

Daniel Bruce Lloyd

Ashutosh Vishwas Kulkarni

Samuel A Minter

Brett E Lounsbury

Siqi Zhao

David Akira Gingrich

Jeffrey B Slosberg

Siu Nam Wong

Tri M Cao

Scott James McKee

Adam Landry Bordelon

Vishal M Shanbhag

Joseph Steven Mate

Michal Maciej Bryc

Michal M Brye

Social network postings, including text, images or other media, may provide valuable information regarding a user of the social network with which the postings may be associated. With the authorization of the user, and upon authentication by the social network, an online marketplace may access the social network postings and extract data therefrom, and market one or more recommended items to the user based on the extracted data, which may include color pallets or texture pallets derived from photographs included in the postings.

Social media data matching

A recommendation system uses artificial intelligence to identify, based on negative sentiment cues from users, item attributes, such as keywords, that users may find offensive or undesirable. The negative sentiment cues may be explicit (e.g., a user selects an option not to view a particular recommendation again), implicit (e.g., a user does not interact with recommendations relating to an attribute), or both. The system may use a computer model generated based on these identified attributes to filter or modify recommendations to a user or group of users. For instance, if a particular keyword is identified as highly offensive to a group of users, items associated with the keyword may be filtered from item recommendations presented to the group of users. If an attribute is identified as moderately offensive to a user, items associated with the attribute may be down-weighted in item recommendations presented to the user.

Artificial intelligence based identification of item attributes associated with negative user sentiment

Artificial intelligence based identification of negative user sentiment in event data

Systems and methods are provided for selecting components to include in portions of a displayable file. Selecting the components may include determining an order of the components for each portion of the displayable file. The components' order for a given portion may be based on a score for each component, where a component's score is based on an estimated value and standard error associated with the component. The component to include in each portion of the displayable file may be selected based at least in part on the determined component order for each portion and a predetermined priority of each portion.

Systems and methods for selecting components for inclusion in portions of a displayable file

A multi-level approach for generating suggestions and/or recommendations associated with one or more items is provided. A group of related items may be identified, for example, by generating a collaborative filtering graph or other graph that includes items and connections between the items. Description data associated with items included in the group may be evaluated in order to determine respective keywords associated with the items and/or a list of common keywords representative of the group of items or a subset of the group of items. Based at least in part upon the list of common keywords, at least one suggestion may be generated.

Clustering and recommending items based upon keyword analysis

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Bothell, WA, United States of America

Stephen Brent Ivie