Narrative-driven recommendation queries are common in question-answering platforms, AI search engines, social forums, and some domain-specific vertical applications.Users typically submit free-form text requests for recommendations, e.g., "Any mind-bending thrillers like Shutter Island you'd recommend?"Such special queries have traditionally been addressed as generic QA task under the RAG paradigm.This work formally introduces narrative recommendation as a distinct task and contends that the RAG paradigm is inherently ill-suited for it, owing to information loss in LLMs when retrieving information from from multiple long and fragmented contexts, and limitations in ranking effectiveness.To overcome these limitations, we propose a novel retrieverank paradigm by theoretically demonstrating its superiority over RAG paradigm.Central to this new paradigm, we specially focus on the information retrieval stage and introduce Opendomain Candidate Generation (OCG)-Agent that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and highquality information.We validate effectiveness of new paradigm and OCG-Agent's retrieve mechanism under real-world datasets from Reddit and corporate education-consulting scenarios.Further extensive ablation studies confirming the rationality of each OCG-Agent component.The code is available at 1 .I am a Chinese student with a Bachelor's degree in CS from BUPT, GPA of 3.3/4, IELTS score of 6.I'm interested in applying for Master's programs related to CS major in Australia.Which universities and programes would I be suitable to apply to?