Abstract: Techniques for inferring a professional reputation for a member of an online social networking service are described. A recommendation graph is generated from professional recommendations submitted by members of the online social networking service for other members of the online social networking service. Using the generated recommendation graph, a reputation model is trained to learn a respective importance for each respective feature of a set of features of electronic profiles on the online social networking service. A professional reputation of a member of the online social networking service is estimated by applying the trained reputation model to a feature vector of the electronic profile of the member, producing a score representing the professional reputation of the member.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which provide for a personalized expertise searching. When a user of the social networking service enters a search query, the system determines if the user is searching for members who possess a particular skill. If the user is searching for members who possess a particular skill, the search results are post-processed by personalizing the search results using one or more machine-learning models which utilize one or more observed features about the user that enters the query, the skills of the members of the social networking service, and the query itself. In some examples, the system may utilize multiple machine-learning models in multiple passes to fine tune the relevance of the search results and to ensure that the post-processing returns search results in a timely manner.

Abstract: Techniques are described herein for deriving, for each member of a social network service, a metric representing the job-seeking propensity of the member. Additionally, techniques for classifying each member with a job-seeking status (e.g., active job-seeker, passive job-seeker, or non-job-seeker) are described. A score-generating algorithm will analyze a variety of input data—including member profile data, social graph data, and activity or behavior data—to derive a job-seeker score, representing the job-seeking propensity of a member. Once derived, the metric is used to customize, personalize or otherwise tailor a user-experience.