How to devise an algorithm that suggests feasible cooking recipes?
Hmm, using Bayes Theorem to make new recipes out of old recipes. I imagine you first would want the algorithm to pull apart the ingredients into a form it understands (not sure if we are using NLP for that, or if you manually enter the data in yourself, that's neither here nor there.) From there...

I envision something like this.

Test Data analyzed. Now we have a list of recipes and the probabilities that each ingredient will take place alongside another ingredient, and in what quantities. After we have this data then you would have the program randomly make the new recipes. First it cycles through a list of all the known ingredients, then randomly selects a main ingredient, from there it uses the probabilities of an ingredient given another ingredient to start throwing in more random ingredients, all the while matching appropriate trained data for quantities and compatible ingredients.

Might I suggest that when creating a new recipe the program be given information like, I want something sweet, or something sour, or for instance something mostly made up of wheat.

Hopefully this helps a bit.