  Food recommendation has become an important means to help guide users to
adopt healthy dietary habits. Previous works on food recommendation either i)
fail to consider users' explicit requirements, ii) ignore crucial health
factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich
food knowledge for recommending healthy recipes. To address these limitations,
we propose a novel problem formulation for food recommendation, modeling this
task as constrained question answering over a large-scale food knowledge
base/graph (KBQA). Besides the requirements from the user query, personalized
requirements from the user's dietary preferences and health guidelines are
handled in a unified way as additional constraints to the QA system. To
validate this idea, we create a QA style dataset for personalized food
recommendation based on a large-scale food knowledge graph and health
guidelines. Furthermore, we propose a KBQA-based personalized food
recommendation framework which is equipped with novel techniques for handling
negations and numerical comparisons in the queries. Experimental results on the
benchmark show that our approach significantly outperforms non-personalized
counterparts (average 59.7% absolute improvement across various evaluation
metrics), and is able to recommend more relevant and healthier recipes.
