Amazon Technologies, Inc.

A9.com, Inc.

Robert G Cottrell

Jeremy Charles York

Brent Russell Smith

Russell Dicker

A subset of a set of components is selected for inclusion on a dynamically-generated page within a particular user context. The subset of components is selected based on scores associated with the components in the set. The score of a component is preferably determined based on measurements of user activity resulting from exposures of the components to users in the particular context.

Adaptive learning methods for selecting page components to include on dynamically generated pages

A subset of a set of components is selected for inclusion on a dynamically-generated web page within a particular user context. Each component may, for example, contain content generated by a respective code module or service. The subset of components is selected based on scores associated with the components in the set. The score of a component is preferably determined based on measurements of user activity resulting from exposures of the components to users in the particular context.

A subset of a set of components is selected for inclusion on a dynamically-generated web page within a particular user context. Each component may, for example, contain content generated by a respective code module or service. The subset of components is selected based on scores associated with the components in the set. The score of a component is preferably determined based on measurements of desired user activity resulting from exposures of the components to users in the particular context, and reflects a likelihood that other users within the same context will perform a desired action. The disclosed methods may also be used to select items, such as products, to suggest to users within specific contexts.

Adaptive learning methods for selecting web page components for inclusion in web pages

Computer processes are disclosed for automatically refining the order and/or content of context-specific lists of items (e.g., products) based on monitored actions of users. One such process involves outputting context-specific lists of items to users together with selectable controls for initiating displays of similar items. When users opt to view similar items, the users' actions performed in connection with the similar items are recorded. If the resulting level of user activity recorded for a particular similar item is sufficiently high, the item is automatically added to the associated context-specific list.

Automated discovery of items associated with a context based on monitored actions of users

A computer-implemented system and method are disclosed that are capable of refining the order and/or content of a list of items. In one embodiment, user activity associated with each item displayed to users in a given context is monitored, and is used together with item exposure data to generate context-specific item weights. The weights for the same item in different contexts may be different. The selection of items included in a list presented to users, and/or the order in which the items in the list are displayed, may be adjusted over time based upon the relative weights associated with the items.

Computer processes for adaptively selecting and/or ranking items for display in particular contexts

The invention comprises a computer-implemented system and method for refining the order and/or content of a list of items. In one embodiment, the amount of user activity associated with each item displayed to users in a given context is monitored, used is to generate context-specific item weights. The weights for the same item in different contexts may be different. The selection of items included in a list presented to users, as well as the order in which the items in the list are displayed, may be adjusted over time based upon the relative weights associated with the items. This allows the items generating the greatest user interest in a particular context to be presented to first, or otherwise emphasized.

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Seattle, WA, United States of America

Robert G Cottrell