公开(公告)号：US10824940B1

公开(公告)日：2020-11-03

申请号：US15365722

申请日：2016-11-30

Applicant: Amazon Technologies, Inc.

Inventor： Oleg Rybakov ,  Siddharth Singh

IPC: G06N3/08 ,  G06N7/00 ,  G06Q30/06

Abstract: The present disclosure is directed to training, and providing recommendations via, a temporal ensemble of neural networks. The neural networks in the temporal ensemble can be trained at different times. For example, a neural network can be periodically trained using current item interaction data, for example once per day using purchase histories of users of an electronic commerce system. The item interaction data can be split into a more recent group and a less recent group, for example the last two weeks of data and the remainder of the last two years of data. The periodic training of neural networks, using updated data and the sliding windows created by the date split, results in a number of different models for predicting item interaction events. Using a collection of these neural networks together as a temporal ensemble can increase recommendation accuracy without requiring additional hardware for training.

公开(公告)号：US10997500B1

公开(公告)日：2021-05-04

申请号：US15603037

申请日：2017-05-23

Applicant: Amazon Technologies, Inc.

Inventor： FNU Vishnu Narayanan ,  Oleg Rybakov ,  Siddharth Singh

IPC: G06N3/08 ,  G06N5/04

Abstract: The present disclosure is directed to generating neural network (NN) output using input data representing various types of events, such as input representing a certain type of event and also an engagement metric that may be representative of a property of the event or representative of a related but different type of event. For example, the output values generated using the NN may be associated with the likelihood that certain future events will occur, given the occurrence of certain past or current events. The output can then be modified (e.g., re-ranked, adjusted, etc.) based on the occurrence of certain other past or current events.