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.

如今，因分解机模型通过将上下文信息纳入推荐建模中来提高推荐性能，因此备受关注。然而，传统的因式分解机器模型通常采用逐点学习方法进行模型参数学习，并且仅对特征之间的线性相互作用进行建模。它们实质上无法捕获功能之间的复杂交互，从而降低了分解机器模型的性能。在这项研究中，我们提出了一种用于项目推荐的神经对成因排序因数分解机NPRFM，它将多层感知神经网络集成到成对成因排序因数分解机模型中。具体来说，要捕获要素之间的高阶和非线性相互作用，我们在双向交互层上堆叠了多层感知神经网络，该网络对要素之间的第二级交互进行编码。此外，不是预测绝对分数，而是采用成对排名模型来学习用户的相对偏好。由于NPRFM没有考虑特征交互的重要性，因此我们提出了NPRFM的新变体，它通过引入注意力机制来学习特征交互的重要性。实际数据集上的经验结果表明，所提出的神经对成对排序因式分解机优于传统的因式分解机模型。采用成对排序模型学习用户的相对偏好。由于NPRFM没有考虑特征交互的重要性，因此我们提出了NPRFM的新变体，它通过引入注意力机制来学习特征交互的重要性。实际数据集上的经验结果表明，所提出的神经对成对排序因式分解机优于传统的因式分解机模型。采用成对排序模型学习用户的相对偏好。由于NPRFM没有考虑特征交互的重要性，因此我们提出了NPRFM的新变体，它通过引入注意力机制来学习特征交互的重要性。实际数据集上的经验结果表明，所提出的神经对成对排序因式分解机优于传统的因式分解机模型。