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LinkedIn uses SLM for dynamic job search facet suggestions

Researchers at LinkedIn have developed a new system called dynamic facet suggestion (DFS) to improve job search relevance. DFS uses a policy-grounded, retrieval-augmented framework with a distilled small language model to offer personalized semantic attributes in real-time. This interactive mechanism helps disambiguate user intent from short, underspecified queries, leading to better job retrieval and engagement. AI

IMPACT Enhances job search relevance by providing personalized semantic attributes, potentially improving user engagement and outcomes.

RANK_REASON Academic paper detailing a new system for job search refinement. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Wenjing Zhang ·

    Policy-Grounded Dynamic Facet Suggestions for Job Search

    Job seekers often initiate search with short, underspecified queries. At LinkedIn, over 80% of job-related queries contain three or fewer keywords, making accurate user intent inference and relevant job retrieval particularly challenging. We present dynamic facet suggestion (DFS)…