In the company I work for there are 2 different evaluation metrics for a song:

  • Yes / No (Equivalent to like/dislike)
  • 1-5 Scale

Customers can use both to rank songs they like. I would like to create a model that predicts the next possible songs you would like. Currently, I'm ignoring the Binary data. I wonder if there's a good way of utilizing the Binary data as tagged data [And not as a feature].

I've thought about two possible solutions:

  • Calculate the 1-5 Scale rating of each user and then take the (mean + std) as 'yes' and (mean - std) as 'no'.
  • Calculate the percentile of 1-5 Scale rating of each user and then take p75 as 'yes' and p25 as 'no'.

In case user doesn't have 1-5 scales I simply ignore him.

I guess that are better ways? (Maybe more empirically correct ways?)



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