Ingredients, Recipes and recipe ratings. I would like to predict the rating based on combination of ingredients I would really appreciate some help on the first steps to my problem, suggestions of modeling techniques i could use or relevant research (i could not find any).

I have a list of ingredients (150 in total) and a list of recipes (100 or so) with data on which combination of ingredients are present in each recipe (on average 4-5 ingredients per recipe).

My output variables are 4 metrics of ratings of those recipes (taste, smell, look, and texture) for each of those 100 recipes from a scale of 1-10 for each.

My aim is to predict the 4 metrics based on a new combination of ingredients (picked from the list) that i specify.

What modeling technique should I look at to help solve this prediction problem?
Here are a few ideas to start with:

A simple model would be to use multiple linear regression (MLR), or a Random Forest 

If you want to evaluate only based on if an ingredient is used, your input dataset could look like this:

Butter   Flour    Eggs  ...  Test   Smell   Look   Texture
1        0        1          1      5       2      4

If you want to predict on how much of each ingredient (some is better, too much is not good) your dataset could look like this:

Butter   Flour    Eggs  ...  Test   Smell   Look   Texture
1.5       3.0        2          1      5       2      4

You can get some other ideas from this website:

https://machinelearningmastery.com/multi-output-regression-models-with-python/