Fast Maximum Margin Matrix Factorization for Collaborative Prediction

by Jason D.M. Rennie and Nathan Srebro

This paper is published just a little after the one in NIPS, the latter aiming at introdcing MMMF. This paper shows how to compute MMMF faster.

There is something strange in the two. The first one in NIPS claims the trace-norm regularizer is good, since it provides global optimal solution (convex optimization solved by SDP). The second one in ICML claims the following is better for computation:

For this non-convex optimization, they use PR-CG. OK... I am an illiterate of optimization techniques.