Personalized Federated Search at LinkedIn
This addresses the challenge of improving search relevance for LinkedIn members by personalizing results based on inferred intents, though it is incremental as it builds on existing federated search with data-driven features.
The paper tackled the problem of personalizing federated search across diverse information sources at LinkedIn by extracting searcher intents from profile data and activities, resulting in significant improvements in member engagement as shown in A/B tests.
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.