The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 28, 2020

Filed:

Jun. 28, 2016
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Leo Parker Dirac, Seattle, WA (US);

Rejith George Joseph, Seattle, WA (US);

Vijai Mohan, Seattle, WA (US);

Oleg Rybakov, Seattle, WA (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 5/02 (2006.01); G06N 20/00 (2019.01); G06Q 50/00 (2012.01); H04L 29/08 (2006.01); H04L 12/58 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01); G06Q 50/01 (2013.01); H04L 51/24 (2013.01); H04L 51/32 (2013.01); H04L 51/36 (2013.01); H04L 67/26 (2013.01);
Abstract

Techniques described herein are directed to improved artificial neural network machine learning techniques that may be employed with a recommendation system to provide predictions with improved accuracy. In some embodiments, item consumption events may be identified for a plurality of users. From these item consumption events, a set of inputs and a set of outputs may be generated according to a data split. In some embodiments, the set of outputs (and potentially the set of inputs) may include item consumption events that are weighted according to a time-decay function. Once a set of inputs and a set of outputs are identified, they may be used to train a prediction model using an artificial neural network. The prediction model may then be used to identify predictions for a specific user based on user-specific item consumption event data.


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