Image aesthetic is a highly subjective task. Thus, generic aesthetics models may lead to inconsistent user agreements even on the same image. Personalized aesthetics models can be employed to remedy the inconsistency issue. In real situation, users shared very small number of annotated images, which makes this problem more challenging. To solve problems above, unlike previous works that focused on user interactive or extracting simple yet effective image features, we address this by meta-learning. Meta-learning is a framework designed for quick adaption of an existing model to a new task with limited labeled data samples. In this way, we can leverage a small amount of annotated data from user and generate an effective personalized aesthetics model quickly. In addition, we proposed a novel meta-learning strategy and a novel meta regularization for our task. Experimental results demonstrate that our approach can effectively learn personalized aesthetics preferences and outperform existing methods on quantitative comparisons with a strong generalization ability.