LLMs Revolutionize Recommendation Systems and Search: A Comprehensive Survey

This article surveys recent research applying Large Language Models (LLMs) to recommendation systems and search engines. Studies explore various approaches, including LLM-augmented model architectures (e.g., YouTube's Semantic IDs and Kuaishou's M3CSR), using LLMs for data generation and analysis (e.g., Bing's Recommendation Quality Improvement and Indeed's Expected Bad Match), and adopting LLM training methodologies (e.g., scaling laws, transfer learning, and knowledge distillation). Furthermore, research focuses on unified architectures for search and recommendation systems, such as LinkedIn's 360Brew and Netflix's UniCoRn, to improve efficiency and performance. Overall, these studies demonstrate the significant potential of LLMs in enhancing recommendation systems and search engines, yielding substantial real-world results.