Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control.

The present disclosure relates to determining the shareability of values of node profiles. Record objects and electronic activities of a system of record corresponding to a data source provider may be accessed. Each record object may correspond to a record object type and have one or more object field-value pairs. Node profiles may be maintained. Values of fields corresponding to a predetermined type of field including fewer than a predetermined threshold number of data source providers may be identified. A restriction tag used to restrict populating other node profiles may be generated. Provision of the value with a second data source provider may be restricted.

Disclosed herein are an artificial intelligence device including a memory configured to store user interest data, a processor configured to generate a keyword combination including at least one of a time keyword, a place keyword, an object keyword or an application type keyword based on the user interest data, and a display configured to display at least one of a time keyword, a place keyword, an object keyword or an application type keyword included in the keyword combination.

A seemingly infinite and continuous stream of online content can be tracked by a movement tracker that can track an amount of movement of a stream of content. For example, such a movement tracker can track the amount of movement per session of a client-side application, such as per session of a web browser. In an example, the tracking of the movement can occur by tracking a measurable parameter of the stream that indicates the amount of movement, such as scroll distance. The movement tracker may also be configured to determine user interaction data according to the tracked amount of movement.

Systems and methods of determining languages of users in networked environments are provided herein. A data processing system having one or more processors coupled with memory can receive, from a client device, a request for content identifying an account profile. The data processing system can determine, using a log record identifying activities of the account profile, a first set of candidate languages. The data processing system can identify a plurality of information resources to be presented in accordance with a ranking. The data processing system can a second set of candidate languages from the plurality of languages based on content in each information resource and a corresponding ranking of each information resource. The data processing system can identify a set of languages included in both the first set of candidate languages and the second set of candidate languages.

A method and apparatus for determining gaze direction information, includes a light source for forming illuminating light to an eye region of a user, and optical element(s) configured to guide the illuminating light from the light source to the eye region. The illuminating light is dynamically adjustable to generate a dynamic light beam on the eye region, and a sensor is configured to capture reflected light on the eye region and generate reflection eye data. The apparatus can maintain user profile information, adjust spectral power distribution of the light source based on the user profile information, receive the reflection eye data, and generate the gaze direction information based on the reflection eye data.

Systems and methods for determining and rewarding accuracy in predicting user-provided ratings of content provided by other users are disclosed. Exemplary implementations may: maintain user accounts associated with users including the first providing user and the second rating user; obtain individual items of user-provided content; effectuate presentations of the individual items of user-provided content through user interfaces to the individual users such that a first presentation is presented, through the second client computing platform, to the second rating user; receive rating information based on input received from the individual users through the user interfaces; determine values for ranking metrics of the individual items of user-provided content; compare the first value for the first ranking metric of the first item with the first rating information; determine, based on the comparison, a first correlation of the first rating information; and distribute an award to the second rating user in accordance with the determined first correlation, responsive to the determined first correlation breaching a threshold.

Systems and methods for providing machine-learning enabled user-specific evaluations are disclosed. Implementations include obtaining a first set of evaluation data from a user interface, obtaining a first set of target-descriptive data including target-specific characteristics objectively describing the evaluation targets, and training, with a machine-learning algorithm, a user-specific evaluation profile indicating evaluation patterns relative to the first set of evaluation data and the first set of target-specific characteristics. Implementations include applying the user-specific evaluation profile to a second set of target-descriptive data to predict a user-specific evaluation.

The present disclosure relates to generating a filtered data set. Data from a plurality of systems of record of a plurality of data source providers may be accessed. A master data set generated using the data accessed from the plurality of systems of record may be maintained. Restriction policies including one or more rules for restricting sharing of data may be maintained. A filtered data set may be generated for a data source provider responsive to an application of restriction policies of other data source providers to the master data set. The filtered data set may be provisioned.

Embodiments of the systems described herein can implement one or more visitor stitching processes. Visitor stitching can include, among other things, one or more processes by which multiple visitors that may appear distinctly independent may be merged into a new single united visitor profile due to the leveraging of one or more unique persistent identifiers.