How to build a model where multiple data points contribute to a result
My intuition is if I arranged the data as individual interactions, with output of final score, that’ll give me what I want. Is that true?


If I understand correctly, your goal is to predict the final score that a customer gives at the end of their dinner based on all the interactions they had during the dinner, right? If yes then I don't think you can keep individual interactions as instances, because then (1) the model would only be able to use one interaction to predict the score and (2) the model would predict a different score for each interaction.

So each instance should represent the full meal and somehow contain as features all the possible information extracted from the interactions. For example an instance could contain the number of interactions, total length, number of refills, etc.

(side note: I wouldn't like eating in a restaurant where customers and staff are monitored so closely, but maybe that's just me)