Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for independently configurable sub-profiles that can be used to express intent within a user profile. A sub-profile is a portion of the user profile that can be assigned an independent set of visibility settings from the other portions of the user profile. A user may add profile data to a sub-profile and select visibility settings for the sub-profile that define access to the profile data included in the sub-profile. Accordingly, access to the profile data included in the sub-profile is managed according to a different set of visibility settings that the profile data included in other portions of the user profile (e.g., profile data not included in the sub-profile). This allows user to make certain profile data available to a different audience of users, either through accessing the user profile and/or through search.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for independently configurable sub-profiles that can be used to express intent within a user profile. A sub-profile is a portion of the user profile that can be assigned an independent set of visibility settings from the other portions of the user profile. A user may add profile data to a sub-profile and select visibility settings for the sub-profile that define access to the profile data included in the sub-profile. Accordingly, access to the profile data included in the sub-profile is managed according to a different set of visibility settings that the profile data included in other portions of the user profile (e.g., profile data not included in the sub-profile). This allows user to make certain profile data available to a different audience of users, either through accessing the user profile and/or through search.

Abstract: A machine may be configured to perform A/B testing on customized experiment units. For example, the machine receives a specification of an experiment unit that identifies a type of subject of an experiment for execution on a social networking service (SNS), and a value of the experiment unit. The machine generates, for the value of the experiment unit, tracking data that tracks user interactions by one or more users of the SNS, via one or more browsers, with content provided during an execution of the experiment. The machine generates, for the value of the experiment unit, metric data that measures an attribute associated with the experimental unit. The machine generates an experiment report based on the tracking data and the metric data. The machine causes a presentation of the experiment report in a user interface of a client device.

Abstract: In some embodiments, the disclosed subject matter involves online, or Web-based, automated assistance with documents using examples, and, more specifically, to providing an online user with automatic and quality ranked examples of content that are related to the context of a document that the user is drafting. The quality criteria may be used to train a model to assist in ranking candidate examples. An embodiment uses a resume assistant add-in to a document editor to provide relevant work experience examples to the user to be rendered on a display in proximity to a resume being edited. The add-in communicates with a backend server via an API, where the backend serve pre-processes available content and stores candidate examples having user selectable criteria in key-value form. Other embodiments are described and claimed.

Abstract: In some embodiments, the disclosed subject matter involves online, or Web-based, automated assistance with documents using examples, and, more specifically, to providing an online user with automatic and quality ranked examples of content that are related to the context of a document that the user is drafting. The quality criteria may be used to train a model to assist in ranking candidate examples ?n embodiment uses a resume assistant add-in to a document editor to provide relevant work experience examples to the user to be rendered on a display in proximity to a resume being edited. The add-in communicates with a backend server via an API, where the backend serve pre-processes available content and stores candidate examples having user selectable criteria in key-value form. Experience examples may be generated to focus on a specific user selectable criteria for inclusion in the displayed examples. Other embodiments are described and claimed.

Abstract: A machine may be configured to facilitate following metrics generated by an A/B testing system. For example, the machine determines, based on a metric identifier of a metric that measures an aspect of a service provided on a Social Networking Service, one or more experiment identifiers of experiments that impact the metric. The machine accesses, based on the metric identifier, a subscription record in a database. The subscription record identifies one or more users who requested to follow the particular metric. The machine identifies, based on the subscription record, a user identifier of a user who requested to follow the metric. The machine generates a digital content item that references the metric and the experiments that impact the metric. The machine causes a presentation of the digital content item in a user interface of a client device associated with the user.

Abstract: A machine may be configured to facilitate following A/B tests generated by an A/B testing system. For example, the machine accesses a subscription record associated with an experiment identifier of an experiment in a database. The experiment may pertain to a service provided on a Social Networking Service (SNS). The subscription record identifies one or more users who requested to follow the experiment. The accessing is based on the experiment identifier. The machine identifies, based on the subscription record, a user identifier of a user of the one or more users who requested to follow the experiment. The machine generates a digital content item that references the experiment followed by the user based on a presentation template and data pertaining to the experiment followed by the user. The machine causes a presentation of the digital content item in a user interface of a client device associated with the user.

Abstract: A machine may be configured to manage alerts related to ramping A/B experiments. For example, the machine identifies an A/B experiment that targets users of a social networking service (SNS). The machine accesses a first value of a metric associated with operation of the SNS. The first value of the metric is generated as a result of a previous execution of the A/B experiment targeting a first segment of users. The machine generates a predicted second value of the metric based on executing a prediction model associated with the A/B experiment. The executing of the prediction model targets a second segment of users that is greater than the first segment. The machine determines that the predicted second value of the metric indicates an inferred negative impact of the A/B experiment on the metric. The machine causes a display of an alert in a user interface displayed on a client device.

Abstract: Techniques for conducting A/B experimentation of online content are described. According to various embodiments, a user specification of a metric being recorded as a result of an online A/B experiment of online content is received, the online A/B experiment being targeted at a segment of members of an online social networking service. Thereafter, a power value for the A/B experiment that is associated with the metric is calculated, the power value indicating an inferred ability to detect changes in a value of the metric during performance of the A/B experiment. The power value for the A/B experiment is then displayed via a user interface displayed on a client device.

Abstract: Techniques for conducting A/B experimentation of online content are described. According to various embodiments, a site-wide impact value for an A/B experiment that is associated with a metric is calculated, the site-wide impact value indicating a predicted percentage change in the value of a metric responsive to application of a treatment variant to an entire portion of a targeted segment of members, in comparison to application of a control variant to an entire portion of the targeted segment of members.

Abstract: Techniques for conducting A/B experimentation of online content are described. According to various embodiments, a user specification of a metric associated with operation of an online social networking service is received. A set of one or more A/B experiments of online content is then identified, each A/B experiment being targeted at a segment of members of the online social networking service. Thereafter, each of the A/B experiments is ranked, based on an inferred impact on the value of the metric in response to application of a treatment variant of each A/B experiment to the online social networking service. A list of one or more of the ranked A/B experiments is then displayed, via a user interface displayed on a client device.