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LinkedIn unveils new GPU retrieval for semantic search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new retrieval framework for semantic search on LinkedIn, aiming to improve the relevance of profile suggestions for users. The system partitions embeddings into eight category-supervised segments, allowing for a more precise matching of user queries to relevant profiles. This approach, implemented with a two-stage GPU architecture using FP8 and FP16 precision, significantly boosts retrieval efficiency and accuracy. In A/B testing, the new framework led to substantial improvements in Precision@10 and Precision@1 for exploratory and navigational queries, respectively. AI

IMPACT This research could lead to more accurate and efficient profile matching on professional networking platforms.

RANK_REASON The item is a research paper detailing a new technical approach for semantic search, not a direct product release announcement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LinkedIn unveils new GPU retrieval for semantic search

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The item is a research paper detailing a new technical approach for semantic search, not a direct product release announcement. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dhritiman Das, Chujie Zheng, Ronak Kaoshik, Pratik Dixit, Vishal Shah, Yanbo Li, Jiahao Xu, Manika Agarwal, Chinmay Naik, Lingyu Zhang, Chetan Bhole, Chirag Bhanuprasad Mehta, Meng Zheng, Puneet Singh Ahluwalia, Shirisha Singh, Ping Jin, Manas Apte, Goku… ·

    Efficient GPU Retrieval for Semantic Search

    arXiv:2608.28968v1 Announce Type: new Abstract: Semantic Search on LinkedIn must retrieve relevant profiles from a corpus of hundreds of millions in response to natural-language queries such as "a fintech founder in Berlin who worked in payments." The deployed relevance policy is…

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