Rebuilding LinkedIn’s Follows Recommendations with LLM-Based Semantic Retrieval and Ranking

This post details the rebuilding of LinkedIn's Follows Recommendation system using LLM-based semantic retrieval and ranking. Key contributions include: transforming profile data into natural language prompts for LLMs, fine-tuning a bi-encoder LLM for follow prediction using supervised contrastive learning (with PEFT, mixed precision, and gradient checkpointing), developing both large-scale offline (Ray, FAISS) and low-latency online (Proxima, hosted vector search) Embedding-Based Retrieval (EBR) pipelines, and integrating LLM embeddings into downstream ranking models via task-aware supervised projection for dimensionality reduction.