DFCN: An Effective Feature Interactions Learning Model for Recommender Systems

Data features in real industrial recommendation scenarios are diverse, high-dimensional and sparse. Effective feature crossing can improve the performance of recommendation, which is of great significance. Manual feature engineering is no longer applicable due to its high cost and low efficiency. Factorization machines introduce the second-order feature interactions to enhance learning ability. Deep neural networks (DNNs) have good nonlinear combination ability and can learn high-order feature interactions. However, DNNs implicitly learn feature interactions at the bit-wise level is not always effective. In this paper, we propose a novel factorization cross network (FCN), which is based on factorization to learn explicit feature crossing through neural network. FCN can learn low- and high-order feature interactions at the vector-wise level with linear time complexity. We introduce deep residual network (DRN) to learn implicit feature interactions. We further use learnable parameters to combine FCN and DRN, and name the new model as deep factorization cross network (DFCN). DFCN can automatically learn low- and high-order explicit and implicit feature interaction information. We have carried out comprehensive experiments on three real-world datasets. Experimental results demonstrate the effectiveness of DFCN, which performs best compared with other competitive models.