LLM-Based Follows Recommendation System

LinkedIn rebuilt its Follows Recommendation system using LLM-based semantic retrieval and ranking. This new system encodes member and creator profiles into embeddings, enabling topical relevance matching over popularity. It includes offline and online embedding-based retrieval pipelines and integrates these embeddings into downstream ranking models through supervised projection layers. The system has shown significant lifts in follow rates, especially for new members. Further optimizations include parameter-efficient fine-tuning (PEFT) with Low-Rank Adaptation, mixed precision training, and gradient checkpointing for efficiency. The retrieval pipeline uses FAISS for exact KNN search in offline scenarios and a hosted vector search service for low-latency online retrieval. Dimensionality reduction for embeddings in ranking models is achieved via task-aware supervised projection layers.