  Factorization Machines (FM), a general predictor that can efficiently model
feature interactions in linear time, was primarily proposed for collaborative
recommendation and have been broadly used for regression, classification and
ranking tasks. Subspace Encoding Factorization Machine (SEFM) has been proposed
recently to overcome the expressiveness limitation of Factorization Machines
(FM) by applying explicit nonlinear feature mapping for both individual
features and feature interactions through one-hot encoding to each input
feature. Despite the effectiveness of SEFM, it increases the memory cost of FM
by $b$ times, where $b$ is the number of bins when applying one-hot encoding on
each input feature. To reduce the memory cost of SEFM, we propose a new method
called Binarized FM which constraints the model parameters to be binary values
(i.e., 1 or $-1$). Then each parameter value can be efficiently stored in one
bit. Our proposed method can significantly reduce the memory cost of SEFM
model. In addition, we propose a new algorithm to effectively and efficiently
learn proposed FM with binary constraints using Straight Through Estimator
(STE) with Adaptive Gradient Descent (Adagrad). Finally, we evaluate the
performance of our proposed method on eight different classification datasets.
Our experimental results have demonstrated that our proposed method achieves
comparable accuracy with SEFM but with much less memory cost.

Machine Learning