Profile Relevance: Boosting Feed Relevance with LLMs at LinkedIn

LinkedIn built Profile Relevance, an unsupervised retrieval system using LLM-generated embeddings to match members with posts outside their network. It compares Rewrite (condensed text, cheaper) versus Non-Rewrite (raw text, richer context) embedding approaches, and appends country names to recover geographic signal lost in rewriting. An AutoEval LLM-judging pipeline scaled evaluation from 20 to 1,000 human-labeled records per model. Incremental profile updates cut compute to 24% of a full-refresh baseline, and horizontal GPU scaling beat vertical scaling. Online tests showed a +0.54% lift in Feed Updates Viewed, +0.94% viral rate, -18.8% hide rate, and +17.7% viral engagement gain for cold-start members.

Source: https://www.linkedin.com/blog/engineering/feed/profile-relevance-boosting-feed-relevance-with-llms-at-linkedin. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.

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