Enhanced factorization machine via neural pairwise ranking and attention networks

The factorization machine models attract significant attention nowadays since they improve recommendation performance by incorporating context information into recommendation modeling. However, traditional factorization machine models often adopt the point-wise learning method for model parameter learning, as well as only model the linear interactions between features. They substantially fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this research, we propose a neural pairwise ranking factorization machine for item recommendation, namely NPRFM, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, instead of the prediction of the absolute scores, the pair-wise ranking model is adopted to learn the relative preferences of users. Since NPRFM does not take into account the importance of feature interactions, we propose a new variant of NPRFM, which learns the importance of feature interactions by introducing the attention mechanism. The empirical results on real-world datasets indicate that the proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models.