Amazon Technologies, Inc.

Siqi Zhao

Adam James Finkelstein

David Michael Hurley

David Akira Gingrich

Stephen Brent Ivie

Siu Nam Wong

Anthony Alexander Santos

Ho Nam Ho

Markus Wai-Keen Kwok

A service may create image-based reviews, which include minimal or no text, to assist customers in researching products. The reviews may include images sorted or grouped (e.g., by sentiment, by product review rating, by age of item, by number of uses, etc.). Images of items may be obtained by the service from user reviews and/or other sources. The images may be associated with text, such as at least some text from associated reviews, commentary, and/or other metadata. The images may be analyzed by a classifier to identify features in the visual image, such as a location of a particular item. The images may be categorized for use in one or more user interfaces that provide image-based item reviews. In some embodiments, the images may be arranged based on a number of uses of the item in the image or by an item age of the item in the image.

Image-based item reviews

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 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

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Seattle, WA, United States of America

Siqi Zhao