Music Recommander using Implicit Library

I want to build a music recommender predicting the number of times a user will listen to a song. I am using the Implicit library and following this close example : https://github.com/benfred/implicit/blob/main/examples/tutorial_lastfm.ipynb

I wanted to know how can I predict the number of plays for a given user for a specific song, all I can see there and in the documentation is to recommend songs to a given user with scores of proximity but without giving the actual prediction

Topic python recommender-system machine-learning

Category Data Science


To continue the hint of @Ipounng I found this : https://stats.stackexchange.com/questions/43942/collaborative-filtering-through-matrix-factorization-with-logistic-loss-function

I tried to run the code they provide with a simple UserID,ArtistID,plays dataset but without great success , if someone already tried this code would love to hear advices

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