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Why we convert rating (1 to 5 or 1 to 10) to Binary Rating System for Collaborative Filtering Recommender Systems and what is benefit

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  • $\begingroup$ Hi @OussamaAlahoum, welcome to the site. Can you provide more context about where you have seen this conversion applied? $\endgroup$
    – noe
    Jun 14 at 10:29
  • $\begingroup$ Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. $\endgroup$
    – Community Bot
    Jun 14 at 16:04

1 Answer 1

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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
    )
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