Abstract: One or more systems and/or methods that provide configurable ad spaces based on configurable content position groups, rather than single content positions, and bid requests containing configuration information describing the position groups, as well as auctions configured to take advantage of the configurability provided by the use of position groups. In an example, a content service platform may receive position group requests and provide auctions mediated by one or more mediation protocols tailored to positions groups and optionally specified by the bid request.

Abstract: One or more computing devices, systems, and/or methods for generating a user-specific transaction interface are provided. In an example, a datastore of a user may be searched for an indication of a potential transaction. The datastore may include an email mailbox and/or a record of web browsing. In response to identifying the potential transaction, the datastore of the user, a partnership datastore and/or a network may be searched for an opportunity associated with the potential transaction. In response to identifying the opportunity associated with the potential transaction, a user-specific transaction interface may be generated. The user-specific transaction interface may include one or more selectable inputs for engaging in a version of the potential transaction. The user-specific transaction interface may be provided for display on a device of the user.

Abstract: One or more computing devices, systems, and/or methods for generating a user-specific interface are provided. In an example, a user-specific machine learning model, for a user of an email application, may be trained based upon one or more interactions of the user with a device upon which the email application is installed. A determination may be made that an email message has been received by an email account of the user. A user-specific message interface may be generated based upon (i) the trained user-specific machine learning model and (ii) content of the email message. A notification of the email message may be provided for display on the device of the user. In response to the user selecting the notification of the email message, the user-specific interface may be provided for display on the device of the user.

Abstract: One or more computing devices, systems, and/or methods for generating a user-specific interface are provided. In an example, a user-specific machine learning model, for a user of an email application, may be trained based upon one or more interactions of the user with a device upon which the email application is installed. A determination may be made that an email message has been received by an email account of the user. A user-specific message interface may be generated based upon (i) the trained user-specific machine learning model and (ii) content of the email message. A notification of the email message may be provided for display on the device of the user. In response to the user selecting the notification of the email message, the user-specific interface may be provided for display on the device of the user.

Abstract: One or more computing devices, systems, and/or methods for generating a user-specific transaction interface are provided. In an example, a datastore of a user may be searched for an indication of a potential transaction. The datastore may include an email mailbox and/or a record of web browsing. In response to identifying the potential transaction, the datastore of the user, a partnership datastore and/or a network may be searched for an opportunity associated with the potential transaction. In response to identifying the opportunity associated with the potential transaction, a user-specific transaction interface may be generated. The user-specific transaction interface may include one or more selectable inputs for engaging in a version of the potential transaction. The user-specific transaction interface may be provided for display on a device of the user.

Abstract: One or more computing devices, systems, and/or methods for generating a user-specific interface are provided. In an example, a user-specific machine learning model, for a user of an email application, may be trained based upon one or more interactions of the user with a device upon which the email application is installed. A determination may be made that an email message has been received by an email account of the user. A user-specific message interface may be generated based upon (i) the trained user-specific machine learning model and (ii) content of the email message. A notification of the email message may be provided for display on the device of the user. In response to the user selecting the notification of the email message, the user-specific interface may be provided for display on the device of the user.