  Cooking typically involves a plethora of decisions about ingredients and
tools that need to be chosen in order to write a good cooking recipe. Cooking
can be modelled in an optimization framework, as it involves a search space of
ingredients, kitchen tools, cooking times or temperatures. If we model as an
objective function the quality of the recipe, several problems arise. No
analytical expression can model all the recipes, so no gradients are available.
The objective function is subjective, in other words, it contains noise.
Moreover, evaluations are expensive both in time and human resources. Bayesian
Optimization (BO) emerges as an ideal methodology to tackle problems with these
characteristics. In this paper, we propose a methodology to suggest recipe
recommendations based on a Machine Learning (ML) model that fits real and
simulated data and BO. We provide empirical evidence with two experiments that
support the adequacy of the methodology.
