Personalized Federated Search at LinkedIn
February 16, 2016 Β· Declared Dead Β· π International Conference on Information and Knowledge Management
"No code URL or promise found in abstract"
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Authors
Dhruv Arya, Viet Ha-Thuc, Shakti Sinha
arXiv ID
1602.04924
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG
Citations
17
Venue
International Conference on Information and Knowledge Management
Last Checked
3 months ago
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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