Amazon Technologies, Inc.

David Michael Hurley

Adam James Finkelstein

Stephen Brent Ivie

Aaron James Dykstra

Siqi Zhao

David Akira Gingrich

Saurabh Nangia

Siu Nam Wong

Ashutosh Vishwas Kulkarni

Scott James McKee

Adam Landry Bordelon

Anthony Alexander Santos

Alexander Slutsker

Aparna Raman

Remo Antonio Cocco

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

Methods, systems, and computer-readable media for a distributed read-only database service are disclosed. Using a read-only database service, one or more host groups are selected from a plurality of available host groups in a distributed system. The one or more host groups are selected for a particular dataset based at least in part on a size of the dataset and on a transaction rate for the dataset. The selected one or more host groups comprise one or more hosts comprising storage resources. A read-only database comprising elements of the dataset is generated. The read-only database is deployed to the storage resources of the one or more host groups in the distributed system. The one or more host groups are configured to serve a plurality of read requests from clients for the elements of the read-only database.

Distributed read-only database service

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

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

A system that identifies attributes of an item depicted in an image using artificial intelligence is provided. For example, the system may use one or more deep belief networks (DBNs) or convolution neural networks (CNNs) trained to analyze images and identify attributes in items depicted in the images. A first artificial intelligence module may analyze an image to determine a type of item depicted in the image. The system may then select a second artificial intelligence module that is associated with the type of item and use the second artificial intelligence module to identify attributes in the item depicted in the image. Identified attributes, if associated with a confidence level over a threshold value, may be provided to a user. The user may provide feedback on the accuracy of the identified attributes, which can be used to further train the first and/or second artificial intelligence modules.

Identification of item attributes using artificial intelligence

Growing community of inventors

Seattle, WA, United States of America

David Michael Hurley