Abstract: Techniques for conducting A/B experimentation of online content are described. According to various embodiments, a user specification of targeting criteria defining a targeted segment of members of an online social networking service for an A/B experiment is received. A set of members of the online social networking service satisfying the user-specified targeting criteria is then identified. Thereafter, a user specification of allocation criteria defining one or more variants of an A/B experiment and one or more corresponding allocation percentages is received. A subset of the set of members (corresponding to the user-specific allocation percentage associated with the respective user-specified variant) is then assigned to each of the user-specified variants. Further, at least one data record associated with each of the variants is recorded in a database, each data record indicating the subset of the set of members assigned to the respective variant.

Abstract: Techniques for conducting A/B experimentation of online content are described. According to various embodiments, it is determined that a member of an online social networking service is accessing electronic content that is associated with an A/B experiment, the member being associated with a given member ID. The given member ID and a request for a correct variant of the A/B experiment for display for the given member ID is transmitted, from a service associated with the online content, to an A/B experiment server. The correct variant of the A/B experiment for display for the given member ID is accessed from a database, and information regarding the correct variant of the A/B experiment is transmitted to the service associated with the online content. Thereafter, the correct variant of the A/B experiment in conjunction with the online content is displayed, to a client device associated with the member.

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.

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 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: Systems and methods for identifying members of a social network service as job referral sources are described. In some example embodiments, the systems and methods may receive or access information associated with an available job, and for a member of a social network that is affiliated with the available job (e.g., is employed by the company providing the available job), identify a connection within the social network that is a suitable job referral source by utilizing an algorithmic process that determines the suitability of members connected to the identified connection as candidates for the available job.

Abstract: Systems and methods for identifying members of a social network service as candidate referral sources are described. In some example embodiments, the systems and methods may receive or access information associated with an available job, and for a member of a social network that is affiliated with the available job (e.g., is employed by the company providing the available job), identify a connection within the social network that is a suitable candidate referral source by utilizing an algorithmic process that determines the suitability of members connected to the identified connection as candidates for the available job.