Questioning is an effective method of extracting information from documents. While Large Language Models excel at answering user queries in document question-answering systems, users often struggle to formulate effective questions when encountering unfamiliar documents. Fortunately, expert-formulated questions embodying professional knowledge can be transferred to new documents to assist ordinary users. Therefore, we propose a question recommendation approach that transfers expert questions on historical documents to new ones, enabling users to “stand on the shoulders of giants” for enhanced document comprehension. Our approach comprises two modules: 1) A “question reusability classification” module identifies domain-general questions applicable across similar documents; 2) A “document-bridged question ranking” module selects semantically appropriate questions for new documents. Experiments on our self-constructed expert question dataset demonstrate that both components significantly impact recommendation accuracy, and performance improves as historical data volume increases.