  In recent years, deep neural network is introduced in recommender systems to
solve the collaborative filtering problem, which has achieved immense success
on computer vision, speech recognition and natural language processing. On one
hand, deep neural network can be used to model the auxiliary information in
recommender systems. On the other hand, it is also capable of modeling
nonlinear relationships between users and items. One advantage of deep neural
network is that the performance of the algorithm can be easily enhanced by
augmenting the depth of the neural network. However, two potential problems may
emerge when the deep neural work is exploited to model relationships between
users and items. The fundamental problem is that the complexity of the
algorithm grows significantly with the increment in the depth of the neural
network. The second one is that a deeper neural network may undermine the
accuracy of the algorithm. In order to alleviate these problems, we propose a
hybrid neural network that combines heterogeneous neural networks with
different structures. The experimental results on real datasets reveal that our
method is superior to the state-of-the-art methods in terms of the item
ranking.
