  LinkedIn has grown to become a platform hosting diverse sources of
information ranging from member profiles, jobs, professional groups, slideshows
etc. Given the existence of multiple sources, when a member issues a query like
"software engineer", the member could look for software engineer profiles, jobs
or professional groups. To tackle this problem, we exploit a data-driven
approach that extracts searcher intents from their profile data and recent
activities at a large scale. The intents such as job seeking, hiring, content
consuming are used to construct features to personalize federated search
experience. We tested the approach on the LinkedIn homepage and A/B tests show
significant improvements in member engagement. As of writing this paper, the
approach powers all of federated search on LinkedIn homepage.
