Personalized Federated Search at LinkedIn
Dhruv Arya, Viet Ha-Thuc, Shakti Sinha

TL;DR
This paper presents a data-driven method for personalizing federated search on LinkedIn by extracting user intents from profile data and activities, significantly improving engagement.
Contribution
It introduces a novel approach to personalize federated search by leveraging user intents derived from large-scale profile and activity data.
Findings
Significant improvement in member engagement from A/B testing
The approach is now used for all federated search on LinkedIn homepage
Effective extraction of user intents from profile and activity data
Abstract
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.
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