With the rising incidence of some diseases, such as obesity and diabetes, the healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, cooking assistance or the nutrition and calorie estimation of dishes, ignoring the personalized health-aware food recommendation. Therefore, in this work, we present a personalized health-aware food recommendation scheme, namely Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home. The proposed scheme comprises three components, namely recipe retrieval, user health profiling, and health-aware food recommendation. In particular, recipe retrieval aims to recognize the ingredients in the micro-videos taken by the users from the market, and then retrieve recipe candidates from a large-scale recipe dataset. Meanwhile, we propose a deep model to characterize the health conditions of users by capturing the textual health-related information crawled from social networks. To solve the issue that the health-related information is extremely sparse, we incorporate a word-class interaction mechanism into the proposed deep model to learn the fine-grained correlations between the textual tweets and pre-defined health concepts. We ultimately present a novel category-aware hierarchical memory network-based recommender to learn the health-aware user-recipe interactions for better food recommendation. Extensive experiments demonstrate the effectiveness of the health-aware food recommendation scheme.