I would like to build a recommendation system:
no ratings are available at the time of recommendation, therefore only a purely context-based recommendation system is needed
as input features answers of a questionnaire are available (all categorical)
My idea is the following:
Find the most similar users based on the answers from the questionnaire with a suitable distance measure.
the past recommendations of these users are relevant and meaningful for the new user in the system
When choosing the encoding and distance measure, I have the problem that there are only categorical variables with values from binary to questions with 20 unique values. One-hot encoding has its drawbacks with multicollinearity and I'm not sure since variables with 20 unique possibilties get such a strong emphasis.
Does anyone have a recommendation for a possible approach? Thanks a lot!