  Recently, Factorization Machines (FM) has become more and more popular for
recommendation systems, due to its effectiveness in finding informative
interactions between features. Usually, the weights for the interactions is
learnt as a low rank weight matrix, which is formulated as an inner product of
two low rank matrices. This low rank can help improve the generalization
ability of Factorization Machines. However, to choose the rank properly, it
usually needs to run the algorithm for many times using different ranks, which
clearly is inefficient for some large-scale datasets. To alleviate this issue,
we propose an Adaptive Boosting framework of Factorization Machines (AdaFM),
which can adaptively search for proper ranks for different datasets without
re-training. Instead of using a fixed rank for FM, the proposed algorithm will
adaptively gradually increases its rank according to its performance until the
performance does not grow, using boosting strategy. To verify the performance
of our proposed framework, we conduct an extensive set of experiments on many
real-world datasets. Encouraging empirical results shows that the proposed
algorithms are generally more effective than state-of-the-art other
Factorization Machines.
