LLM as User Simulator: Towards Training News Recommender without Real User Interactions

News recommendation systems traditionally rely on extensive real user interaction data to personalize content, which is often inaccessible and raises privacy concerns, particularly in regions lacking such data. To address these challenges, we propose LAUS (LLM As User Simulator), a novel framework that leverages LLM to simulate user interactions for training a news recommender without real user data. Our framework consists of two stages. First, we simulate user clicks by using an LLM to generate click probabilities for candidate news articles based on simulated user reading histories, with candidate sets generated through random sampling and embedding-based retrieval. Second, we train a lightweight news recommender using these simulated user interactions. Experiments on three datasets demonstrate that a news recommender trained with simulated data outperforms models using LLM prompting, while significantly reducing recommendation time per user. Our findings highlight the potential of using LLMs as user simulators to address data scarcity in news recommendation systems.