I am trying to build a model that delivers a prediction for which food a person may like based on information the person has entered into the system beforehand, e.g. they input that they like (beef, chicken, curry, rice, chili, peas, onions etc.) and get recommended a dish that they might like.
The dataset was gathered from a survey asking participants to list ingredients they like, and to rate dishes on a scale from 1-5 (5 beeing best).
The prediction should return a dish from the dishes rated in the survey for the ingredients a new user entered.
So far I am trying to solve the problem with a multinomial logistic regression. Am I right in this assumption or is there a model significantly better suited for the problem?
I fear that the interpretation will be a problem, as I have around 40 dishes, meaning 40 categorical dependent variables. Can this be problematic? Statistical significance is not of utmost importance, as this is a proof of concept trial run with small sample size (n below 100), to test the data structure and the suitability of the model. Once these are set, collection of data will be outsourced.