Solved – Adding content information to matrix factorization-based recommender

Question

I'm currently using a matrix factorization method to generate recommendations (for info on this, check: Matrix Factorization Techniques for Recommender Systems). At the moment, my rating estimate is given by the global mean, user and item biases, and the latent factors.

I'm interested in adding content information to this system. In this case, both my items and users can be represented in a topic (word) space. I was thinking about adding the correlation between user and item (some value in [0,1]) as an additional input source to my rating estimation.

My question is: how to best include this information? just add it to the sum? Is it not a problem if the correlation is a value between 0 and 1, and the global mean and biases are values that can be as big as 100 ?

Thanks!

Answer 1

This paper describes one approach you might consider. There is a python implementation that's easy to use.