I have a binary matrix N x N where both rows and columns represent users of a website. If matix[i,j] = 1 it means that user i liked the user j (posts). On the other sied, not necessarily matix[j,i] = 1 because it could be that user j did not like user i' posts. I would like, given a user k, to suggest the top 10 users to follow. I think this problem can be solved using a collaborative filtering. I did some research, but I only found solutions based on matrices where columns and rows contains different items, e.g. users-songs, users-movies, users-items, etc. Would it make sense to apply what is commonly known as ITEM-ITEM (as described in this post)?:

Item-item summary:

  1. Normalize user vectors to unit vectors.
  2. Construct a new item by item matrix.
  3. Compute the cosine similarity between all items in the matrix.

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