Amazon Technologies, Inc.

Anirudh Dastidar

Andrew J Bradley

Anthony Richard McBryan

Sebastiano Merlino

Andrew Douglas Birkett

Fred Grossman

Sean Daniel Murphy

Fady Mohamed

Systems, methods, and computer-readable media are disclosed for filtering data with probabilistic filters for content selection. In one embodiment, an example method may include determining a user interaction history with a first product identifier for a user account, determining a first parent product identifier of the first product identifier, and generating a database with the first parent product identifier and a user account identifier for the user account. Example methods may include determining a set of candidate content with first content and second content for the user account, determining a second product identifier associated with the first content, and determining a second parent product identifier of the second product identifier. Example methods may include determining that the second parent product identifier is not present in the database using a probabilistic filter, and determining that the first content is eligible for presentation.

Filtering data with probabilistic filters for content selection

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.

Parallel data pool processing and intelligent item selection

Systems, methods, and computer-readable media are disclosed for preventing duplicate content selection for digital presentation. In one embodiment, an example method may include determining a first bid request for a first content delivery slot at a first time, the first bid request having a first user identifier, determining a ranking of candidate product identifiers for which the first user identifier is eligible at the first time, and determining a second bid request for a second content delivery slot at a second time, the second bid request having the first user identifier, where the second time is within a predetermined time interval of the first time. Example methods may include determining an impression counter value for the first user identifier at the second time, selecting a product identifier from the candidate product identifiers using the impression counter value, and determining a first bid amount using the product identifier.

Preventing duplicate content selection for digital presentation

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West Midlands, United Kingdom

Anirudh Dastidar