I have data on users and bands they listened to. Best way to create embedding of users and artists?

I am using a Lastfm dataset where there are U users and M artists:

  1. (user, user) edges where an edge exist if there is a mutual following between users
  2. (user, artist) edges where an edge exists if a user listens to an artist.

Goal: I want to represent both users and artists in the same latent space to be able to say how similar an artist is to a user’s tastes.

  1. One approach is to create a utility matrix of dimension of dimension U x M where (u,m) = 1 if user listened to artist and 0 otherwise. Then I can factorize this matrix with something like Alternating Least Squares.

  2. Another approach is to use a model of heterogenous graphs such as metapath2vec, which creates embeddings on heterogenous graphs from taking random walks.


The advantage of the first option is that it’s simpler. However I believe there should be some extension of collaborative filtering that can cover this case.

The advantage of the second option is that it can utilize (user, user) edges.

This topic was automatically closed 30 days after the last reply. New replies are no longer allowed.