Collaborative filtering doesn't work for us
This doesn&#x27;t even come close to the power of (not even particularly) modern collaborative filtering algorithms.<p>CF doesn&#x27;t need to assume that A talks to B well and B talks to C well implies A talks to C well- rather, it typically tries to learn an embedding for A, B, C that predicts their interactions effectively.<p>In the simplest form, this could be applying SVD or nonnegative matrix factorization to the user interaction matrix, truncating to get a low-rank symmetric approximation. More advanced forms would try to predict the interaction as some function of the two user embeddings, possibly even a neural network.<p>This only shows that a particular feature based on 2-chains of interactions isn&#x27;t useful, whereas the standard approaches I described can try to infer information across any number of connections, and don&#x27;t necessarily assume that 2-chains as described are indicative of a particular result.