Over the last few years, the deep neural network is utilized to solve the collaborative filtering problem, a method of which has achieved immense success on computer vision, speech recognition as well as natural language processing. On one hand, the deep neural network can be used to capture the side information of users and items. On the other hand, it is also capable of modeling interactions between users and items. Most of existing approaches exploit the neural network with solo structure to model user–item interactions such that the learning representation may be insufficient over the extremely sparse rating data. Recently, a large number of neural networks with mixed structures are devised for learning better representations. A carefully designed hybrid network is able to achieve considerable accuracy but only requires a small amount of extra computation. In order to model user–item interactions, we elaborate a hybrid neural network consisting of the global neural network and several local neural blocks. The multi-layer perceptron is adopted to build the global neural network and the residual network is used to form the local neural block which is inserted into two adjacent global layers. The hybrid network is further combined with the generalized matrix factorization to capture both the linear and nonlinear relationships between users and items. It is verified by experimental results on benchmark datasets that our method is superior to certain state-of-the-art approaches in terms of top-n item recommendation.

近年来，利用深度神经网络解决协同过滤问题，该方法在计算机视觉、语音识别和自然语言处理方面取得了巨大成功。一方面，深度神经网络可以用来捕捉用户和物品的边信息。另一方面，它还能够对用户和项目之间的交互进行建模。大多数现有方法利用具有单独结构的神经网络来对用户-项目交互进行建模，因此对于极其稀疏的评分数据，学习表示可能不足。最近，设计了大量具有混合结构的神经网络来学习更好的表示。精心设计的混合网络能够达到相当高的精度，但只需要少量的额外计算。为了对用户-项目交互进行建模，我们精心设计了一个由全局神经网络和几个局部神经块组成的混合神经网络。采用多层感知器构建全局神经网络，利用残差网络形成局部神经块，插入相邻的两个全局层中。混合网络进一步结合广义矩阵分解来捕捉用户和物品之间的线性和非线性关系。基准数据集的实验结果证实，我们的方法在 top-n 项目推荐方面优于某些最先进的方法。我们阐述了一个由全局神经网络和几个局部神经块组成的混合神经网络。采用多层感知器构建全局神经网络，利用残差网络形成局部神经块，插入相邻的两个全局层中。混合网络进一步结合广义矩阵分解来捕捉用户和物品之间的线性和非线性关系。基准数据集的实验结果证实，我们的方法在 top-n 项目推荐方面优于某些最先进的方法。我们阐述了一个由全局神经网络和几个局部神经块组成的混合神经网络。采用多层感知器构建全局神经网络，利用残差网络形成局部神经块，插入相邻的两个全局层中。混合网络进一步结合广义矩阵分解来捕捉用户和物品之间的线性和非线性关系。基准数据集的实验结果证实，我们的方法在 top-n 项目推荐方面优于某些最先进的方法。采用多层感知器构建全局神经网络，利用残差网络形成局部神经块，插入相邻的两个全局层中。混合网络进一步结合广义矩阵分解来捕捉用户和物品之间的线性和非线性关系。基准数据集的实验结果证实，我们的方法在 top-n 项目推荐方面优于某些最先进的方法。采用多层感知器构建全局神经网络，利用残差网络形成局部神经块，插入相邻的两个全局层中。混合网络进一步结合广义矩阵分解来捕捉用户和物品之间的线性和非线性关系。基准数据集的实验结果证实，我们的方法在 top-n 项目推荐方面优于某些最先进的方法。