  Everyone eats. However, people do not always know what to eat. They need a
little help and inspiration. Consequently, a number of apps, services, and
programs have developed recommenders around food. These cover food, meal,
recipe, and restaurant recommendations, which are the most common use cases,
but also other areas such as substitute ingredients, menus, and diets. The
latter is especially important in the area of health and wellness where users
have more specific dietary needs and goals.
  In this survey, we review the food recommender literature. We cover the types
of systems in terms of their goals and what they are recommending, the datasets
and signals that they use to train models, the technical approaches and model
types used, as well as some of the system constraints.
