Large Scale Search

Large-scale search aims to efficiently and effectively retrieve relevant information from massive datasets, focusing on improving accuracy, novelty, and speed. Current research emphasizes developing advanced learning-to-rank models, often incorporating transformer networks, graph neural networks, and large language models, to handle diverse data types and user queries, and address challenges like cold starts and biased feedback. These advancements are crucial for enhancing the performance of web search engines, recommendation systems, and other applications that rely on efficient information retrieval, ultimately impacting user experience and the accessibility of information.