In the era of information explosion, most users urgently need timely and effective recommendation service. As a result, the number of interactions, which a recommender system requires to recognize the drifted interests of existing users or the unknown preference of new users, would largely determine the application’s survival rate in the highly competitive consumer market. However, for the best of our knowledge, this responsiveness aspect of recommender system is far from well-studied. To bridge this gap, we propose a task-based meta learning approach towards responsive recommender system, which helps improve the recommendation quality for both existing and new users after the system only observes a limited number of incoming interactions. Basically, the leverage of meta learning contributes to the fast adaption to the optimum of the underlying model with few interactions to satisfy the responsiveness requirement. Extensive experiments on MovieLens and Netflix datasets highly demonstrate the responsiveness of the proposed method.