One or more computing devices, systems, and/or methods for content recommendation based upon continuity and grouping information of attributes are provided herein. User interaction data specifying whether users interacted with content items, user attributes of the users, and content attributes of the content items is obtained. A data structure is populated with the user interaction data. The data structure is modified by inserting a set of sub-fields into the data structure for a user attribute. A sub-field is populated with a value representing an option of the user attribute. The set of sub-fields are an encoding of continuity information and grouping information representing options for the user attribute. The data structure is processed using machine learning functionality to generate a model. The model is utilized to generate a prediction as to whether a user will interact with a content item.

This disclosure relates to methods, non-transitory computer readable media, and systems that generate and visualize bias scores within segment-generation-user interfaces prior to executing proposed actions with regard to target segments. For example, the disclosed systems can generate a bias score indicating a measure of bias for a characteristic within a segment of users selected for a proposed action and visualize the bias score and corresponding characteristic in a segment-generation-user interface. In some implementations, the disclosed systems can further integrate detecting and visualizing bias as a bias score with selectable options for a segmentation-bias system to generate and modify segments of users to reduce detected bias.

Systems and methods can suggest applications and/or recipients for a user of a computing device to perform communication. The suggestions can be provided on a user interface for a user to select. A suggestion engine can use historical user interactions that include a recipient, a communication application used to communicate with the recipient, and contextual data to determine which application and/or recipients to suggest. The user interactions may occur in a variety of ways, e.g., after a content object has been selected within a host application, where a communication application is selected thereafter. Multiple models may be used to provide the suggestions, such as a pattern model or a cluster model that uses recent user interactions. As another example, a heuristics model may also be used.

A method for sorting geographic location points, a method for training a sorting model and corresponding apparatuses are disclosed, which relates to the technical field of big data. A specific implementation solution is: receiving a query request for geographic location points of a vertical class from a user; inputting candidate geographic location point data of the vertical class into a preference model of the user, to obtain a preference score of the user for each candidate geographic location point; inputting the preference score of the user for each candidate geographic location point into a sorting model as one of sorting features of each candidate geographic location point, to obtain a sorting score of each candidate geographic location point; and determining, according to the sorting score of each candidate geographic location point, a query result returned to the user. The present disclosure can integrate preference factors of a user into sorting when the user queries geographic location points of a vertical class, so that query results can meet the user's personalized needs.

Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.

Various client devices include displays and one or more image capture devices configured to capture video data. Different users of an online system may authorize client devices to exchange information captured by their respective image capture devices. Additionally, a client device modifies captured video data based on users identified in the video data. For example, the client device changes parameters of the image capture device to more prominently display a user identified in the video data and may further change parameters of the image capture device based on gestures or movement of the user identified in the video data. The client device may apply multiple models to captured video data to modify the captured video data or subsequent capturing of video data by the image capture device.

A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.

A system and method for in-person proximity matching that allows a plurality of user electronic devices to identify and match with one another based on a compatibility profile. The matching is done using a near-field communication protocol, such as Bluetooth or Wi-Fi. The invention allows matching of users when they are within the near proximity, which is defined as approximately 1000 meters.

Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Systems and methods are provided for sharing a screen from a mobile device. For example, a method includes receiving, at a second mobile device, an image of a screen captured from a first mobile device and determining whether to trigger an automated action. The method may also include displaying, responsive to not triggering the automated action, annotation data generated for the image with the image on a display of the second mobile device, the annotation data including at least one visual cue corresponding to content in the image relevant to a user of the second mobile device. The method may further include, responsive to triggering the automated action, determining that a mobile application associated with the image is installed on the second mobile device and replaying user input actions received with the image on the second mobile device starting from a reference screen associated with the mobile application.