Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful
learning approach to this task and has been recently extended to structured ranking losses.
In this paper we discuss a number of extensions to MMMF by introducing offset terms, item
dependent regularization and a graph kernel on the recommender graph. We show
equivalence between graph kernels and the recent MMMF extensions by Mnih and …