  With the rising incidence of some diseases, such as obesity and diabetes, a
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 acquire the ingredients available to the users, and then
retrieve recipe candidates from a large-scale recipe dataset. User health
profiling is to characterize the health conditions of users by capturing the
textual health-related information crawled from social networks. Specifically,
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. For the health-aware food recommendation, we present a novel
category-aware hierarchical memory network-based recommender to learn the
health-aware user-recipe interactions for better food recommendation. Moreover,
extensive experiments demonstrate the effectiveness of the health-aware food
recommendation scheme.
