Amazon Technologies, Inc.

Andrew Douglas Birkett

Andrew J Bradley

Anthony Richard McBryan

Sebastiano Merlino

Anirudh Dastidar

Jason A Kilar

Matthew J Round

Graeme N Smith

Michael Davidson

Gregory Huczynski

Jason King

Fred Grossman

Haakon Sorensen

Elinor Trotter

Tom Killalea

Janna Hamaker

Sean Daniel Murphy

Christopher F Weight

Shane Girish

Christopher De Beer

Fady Mohamed

Alexander William Robb Nelson

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 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

Techniques are described for identifying book title sets. The techniques may include a first-pass comparison with other books to identify other candidate title sets. A second-pass comparison may then be performed with respect to the candidate title sets. The first-pass comparison may be based on book metadata such as titles and authorship. The second-pass comparison may include a more comprehensive content comparison, such as comparing the body text of the books.

Identifying book title sets

A method and system are disclosed for allowing a user to suggest information about content that can be utilized in processing subsequent searches. The suggested information may be different from a fixed set of information about an item, such as keywords, attributes or characteristics of an item, generally provided by a manufacturer or seller of the item. Users can submit to a service the information that they feel is relevant to the item, such as a brief explanation of relevance and a detailed explanation that would help other users to make a decision about the item. The submitted information may be associated with the item information and stored in a data store and utilized as additional attributes of the item for a search. In addition, when a user requests detail information about an item, the user-submitted information may be displayed to the user.

Enhanced search with user suggested search information

A method and system are disclosed for allowing a user to suggest information about content that can be utilized in processing subsequent searches. The suggested information may be different from a fixed set of information about an item, such as keywords, attributes, or characteristics of an item, generally provided by a manufacturer or seller of the item. Users can submit to a service the information that they feel is relevant to the item, such as a brief explanation of relevance and a detailed explanation that would help other users to make a decision about the item. The submitted information may be associated with the item information and stored in a data store and utilized as additional attributes of the item for a search. In addition, when a user requests detail information about an item, the user-submitted information may be displayed to the user.

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Edinburgh, United Kingdom

Andrew Douglas Birkett