Why we convert rating (1 to 5 or 1 to 10) to Binary Rating System for Collaborative Filtering Recommender Systems and what is benefit
You do not have to convert rating to the binary value to use in Collaborative Filtering. The result of the prediction will be the predicted rating for each user/item, and it can be in range 1 to 5, 1 to 10 or 0 to 1, depending on the data you provide. You, then, sort the output and recommend the items with the highest predicted ratings on top.
Here is an example from fastai collablearner
learn = collab_learner( dls, n_factors=50, # here is the range of you ratings, you typically add 10% to the upper end y_range=(1, 5.5) ) learn.fit_one_cycle( 5, 0.01 )