How LinkedIn Uses Embedding

The article discusses the evolution of LinkedIn's edge-building system, focusing on how it leverages AI-powered recommendations to enhance user interactions.

The article discusses JUDE, LinkedIn's platform for generating high-quality embeddings for job recommendations using fine-tuned Large Language Models (LLMs).

The article discusses Liger-Kernel, an open-source library designed to enhance GPU efficiency for training large language models (LLMs).

This article discusses the Candidate Generation (CG) stage of LinkedIn's People You May Know (PYMK) recommendation system, detailing the various techniques used to generate relevant candidate pools...

The article discusses the development of a new AI-powered experience at LinkedIn, focusing on the challenges and successes encountered while building a generative AI product.

The article discusses the evolution of LinkedIn's collaborative articles, focusing on how feedback from users has shaped the matching system that connects articles with member experts.

The article discusses how LinkedIn utilizes embedding-based retrieval (EBR) technology to enhance job matching for seekers.

The article discusses Pensieve, an embedding feature platform developed by LinkedIn for pre-computing and publishing entity embeddings used in AI models for Talent Solutions and Careers.

The article discusses the development of In-Product Help at LinkedIn, which aims to improve member productivity by providing contextual assistance without requiring users to leave their current tas...