Personalized Federated Search at LinkedIn

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.