公开(公告)号：US10559001B1

公开(公告)日：2020-02-11

申请号：US14747451

申请日：2015-06-23

Applicant: Amazon Technologies, Inc.

Inventor： Andrew J. Bradley ,  Andrew Craig Brind ,  Anthony Richard McBryan ,  Sebastiano Merlino ,  Sean Daniel Murphy ,  Alistair Francis Smith ,  David Neil Turner

IPC: G06Q30/02

Abstract: This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

公开(公告)号：US10936258B1

公开(公告)日：2021-03-02

申请号：US16737730

申请日：2020-01-08

Applicant: Amazon Technologies, Inc.

Inventor： Andrew J. Bradley ,  Andrew Craig Brind ,  Anthony Richard McBryan ,  Sebastiano Merlino ,  Sean Daniel Murphy ,  Alistair Francis Smith ,  David Neil Turner

IPC: G06F3/12 ,  G06Q10/06

Abstract: This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.

公开(公告)号：US11113730B1

公开(公告)日：2021-09-07

申请号：US15457525

申请日：2017-03-13

Applicant: Amazon Technologies, Inc.

Inventor： Andrew Douglas Birkett ,  Andrew J. Bradley ,  Anirudh Dastidar ,  Anthony Richard McBryan ,  Sebastiano Merlino ,  Fady Mohamed ,  Sean Daniel Murphy

IPC: G06Q30/02 ,  G06N5/04

Abstract: Systems, methods, and computer-readable media are disclosed for parallel data pool processing and intelligent item selection. In one embodiment, an example method may include determining a first bid request comprising a first user identifier, determining a first set of product identifiers in a first user interaction history of the first user identifier, determining a second bid request comprising a second user identifier, and determining a second set of product identifiers in a second user interaction history of the second user identifier. Example methods may include determining estimated values for one or more product identifiers in the first set of product identifiers and the second set of product identifiers in parallel, and generating respective first and second responses to the first bid request and the second bid request using the estimated values.

公开(公告)号：US11256453B1

公开(公告)日：2022-02-22

申请号：US17169612

申请日：2021-02-08

Applicant: Amazon Technologies, Inc.

Inventor： Andrew J. Bradley ,  Andrew Craig Brind ,  Anthony Richard McBryan ,  Sebastiano Merlino ,  Sean Daniel Murphy ,  Alistair Francis Smith ,  David Neil Turner

IPC: G06F3/12 ,  G06Q30/02 ,  G06Q10/06

Abstract: This disclosure describes systems, methods, and computer-readable media related to retargeting online advertisement campaign recommendations for advertisements with multiple items or services. Bids may be based on a combined advertisement creative comprising two or more items or services. Dynamically selecting multiple items at bid time using a retargeting model to determine a potential revenue generation amount associated with an event may increase the probability of a conversion event based on the creative that includes the selected items. In some embodiments, a machine-learned retargeting model may be used to select multiple items to be displayed in an advertisement. The retargeting model may be applied to items that were previously viewed by the consumer and may determine a value for each of the items using factors. A bid may be calculated for each of the selected items using the values determined by the retargeting model.