  We examined the use of the Ising model as an $L_0$ regularization method for
field-aware factorization machines (FFM). This approach improves generalization
performance and has the advantage of simultaneously determining the best
feature combinations for each of several groups. We can deepen the
interpretation and understanding of the model from the similarities and
differences in the features selected in each group.
