A server system for a map-based social media platform maintains user location information to enable the rendering of friend icons on a map at a corresponding display locations. The system maintains a per user access control list (ACL) that lists all users whose icons can be viewed by a requesting user. The ACL can include a designation of respective display granularity levels for different friend users.

A server system for a map-based social media platform maintains user location information to enable the rendering of friend icons on a map at a corresponding display locations. The system maintains a per user access control list (ACL) that lists all users whose icons can be viewed by a requesting user. The ACL can include a designation of respective display granularity levels for different friend users.

First data indicative of a first plurality of transactions by a user may be processed to generate first behavioral information describing the user. The first behavioral information may be displayed by an interactive user interface. A user input made in response to the first behavioral information may be received and analyzed to generate user preference information indicating a relationship between the first user input and the first behavioral information. Second data indicative of a second plurality of transactions by the user may be received and processed with the user preference information to generate second behavioral information describing the user. The second behavioral information may be displayed by the interactive user interface differently from the first behavioral information by the interactive user interface as a result of the processing of the second data and the user preference information together.

First data indicative of a first plurality of transactions by a user may be processed to generate first behavioral information describing the user. The first behavioral information may be displayed by an interactive user interface. A user input made in response to the first behavioral information may be received and analyzed to generate user preference information indicating a relationship between the first user input and the first behavioral information. Second data indicative of a second plurality of transactions by the user may be received and processed with the user preference information to generate second behavioral information describing the user. The second behavioral information may be displayed by the interactive user interface differently from the first behavioral information by the interactive user interface as a result of the processing of the second data and the user preference information together.

An electronic device for a first session of a user, for each of a plurality of lists of media content items, determines a respective value for each objective of a first set of objectives and a second set of objectives by accessing contextual data for the first session of the user. The first set of objectives corresponds to the user and the second set of objectives corresponds to a second party distinct from the user. The electronic device, using a multi-arm bandit model, identifies a first list of media content items, from the plurality of lists of media content items, to present to the user, including: calculating a score for each list in the plurality of lists of media items; and probabilistically selecting the first list of media content items according to the respective scores corresponding to the respective lists in the plurality of lists of media items.

An electronic device for a first session of a user, for each of a plurality of lists of media content items, determines a respective value for each objective of a first set of objectives and a second set of objectives by accessing contextual data for the first session of the user. The first set of objectives corresponds to the user and the second set of objectives corresponds to a second party distinct from the user. The electronic device, using a multi-arm bandit model, identifies a first list of media content items, from the plurality of lists of media content items, to present to the user, including: calculating a score for each list in the plurality of lists of media items; and probabilistically selecting the first list of media content items according to the respective scores corresponding to the respective lists in the plurality of lists of media items.

Implementations set forth herein relate to providing selectable autofill suggestions, which correspond to application actions that are at least partially fulfilled using server command data—prior to a user selecting a particular selectable autofill suggestion. Proactively fulfilling command data in this way mitigates latency between user selection of a suggestion and fulfillment of a particular action. Initially, a partial input can be processed to generate autofill suggestions, which can be communicated to a server device for further processing. The autofill suggestions can also be rendered for selection at a touch display interface, thereby allowing a user to select one of the autofill suggestions. As command fulfillment data is provided by the server, the command fulfillment data can be available to a corresponding application(s) in order that any corresponding actions can be at least partially fulfilled prior to user selection.

A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

A user computing entity executes application program code to cause display of an IUI via a user interface of the user computing entity. The IUI comprises an action list comprising one or more action items corresponding to one or more team members of a team. The action items are automatically ordered based on one or more action priorities. At least one of the action items corresponds to a coaching opportunity and a recommendation for responding thereto. The coaching opportunity is automatically identified using a recommendation model trained using machine learning based at least in part on performance data corresponding to a plurality of key performance indicator metrics. The recommendation for responding to the coaching opportunity is determined using the recommendation model and based on the performance data. The recommendation model is trained using information regarding previous handlings of coaching opportunities and corresponding outcome indicators for a cluster of teams.

A computer-implemented method for automating service provider status and reporting during a service visit includes the initial steps of creating a service provider transaction, initiating the transaction, and calculating an estimated completion time of the transaction. The estimated completion time is based on at least one service condition, which may include the availability of servicing tools and components, the availability of service provider employees, the priority status, if any, of the service provider transaction, and the level of difficulty of service provider transaction, among others. Preferably, the service conditions include constant or variable associated values. The completion time is calculated based upon a sum of these values. If an unexpected service need or service delay arises, the service provider transaction status is updated, which includes recalculating the estimated time of completion based on a new service condition that arose from the unexpected need or delay. When the service provider transaction is complete, the customer reviews the transaction, confirms that the service provider transaction is complete, and schedules a service completion event.