Amazon Technologies, Inc.

Andrew J Bradley

Anthony Richard McBryan

Sebastiano Merlino

David Neil Turner

Sean Daniel Murphy

Andrew Douglas Birkett

Alistair Francis Smith

Andrew Craig Brind

Anirudh Dastidar

Matthew J Round

Robert Philip Japp

Oliver Masting

Árdís Elíasdóttir

Fred Grossman

Christopher De Beer

Shane Girish

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

A computing device is configured to generate a content reference for content displayed on a webpage. An encrypted reference is generated for the content reference. The content is provided, with the content reference, to a client device. A website is provided for the client device upon selection of the content by a user input on the webpage. The website includes an offering and related offerings associated with the content. In accordance with a web action received from the website, a logical table is updated with an entry associated with the encrypted reference. The update may be associated with a credit or a charge based at least in part on the web action for the offering or the related offerings.

Privacy-based content tracker

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.

Retargeting events service for online advertising

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 caching selected data for use in real-time content selection. In one embodiment, an example method may include determining a first set of user identifiers for users that are eligible to be presented with content associated with a first product identifier, the first set having a first number of user identifiers, determining a second set of user identifiers for users that are eligible to be presented with content associated with a second product identifier, the second set having a second number of user identifiers, and determining that the first number is greater than the second number. Example methods may include determining first content associated with the first product identifier, determining first product information associated with the first product identifier, and caching the first content and the first product information at a server instead of second content associated with the second product identifier.

Caching selected data for use in real-time content 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

This disclosure describes systems, methods, and computer-readable media related to online advertisement campaign recommendations. A server may obtain data from an events datastore indicative of missing time slices from a partition of events data. The server may obtain first events data for the missing time slices. The server may identify redundant events data in the first events data that is redundant to second events data stored in the events datastore. The server may delete the redundant events data from the first events data to generate a modified first events data. The server may transmit the modified first events data to the events datastore.

Events service for online advertising

This disclosure describes systems and associated methods that can selectively filter seed behavior, e.g., user activity used to generate item recommendations. In some embodiments, seed behaviors and catalog items are associated with categories in an electronic catalog, and a particular seed behavior is used to generate user recommendations if it is more recent than a user's last purchase in the category of the seed behavior. For example, a user's activity in the TV category, e.g., viewing various TV models, may not be used to generate recommendations if the activity occurred prior to the user's purchase of a TV. As a result, in certain embodiments, additional TVs may not appear in the user's recommendations following her purchase, reflecting that the user has fulfilled her desire to purchase a TV.

Growing community of inventors

Edinburgh, United Kingdom

Andrew J Bradley