Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

Abstract: The disclosed embodiments provide a system for improving use of a social network. During operation, the system identifies skills that are trending within a social network based on usage features associated with usage of the skills in the social network. Next, the system matches one or more of the skills to member features for a member of the social network. The system then outputs a recommendation of the skill(s) to the member.

Abstract: The disclosed embodiments provide a system for improving use of a social network. During operation, the system obtains job histories for members of the social network. Next, the system aggregates a set of job transitions in the job histories to obtain a set of job transition trends associated with the members. The system then matches a job transition trend in the set of job transition trends to member features for a member of the social network. Finally, the system outputs the job transition trend as a recommendation for advancing a career of the member.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Social Proofing Engine (hereinafter “SP Engine”) that determines whether to update a target member account's profile with an attribute that is relevant to a resource accessed by the target member account. The SP Engine detects access by a target member account of a resource. The SP Engine identifies at least one trusted member account having a profile context attribute indicative of a type of affiliation with the resource. The SP Engine generates a prompt to update the target member account with the profile context attribute indicative of the type of affiliation with the resource.