  Collaborative Filtering (CF) is widely used in recommender systems to model
user-item interactions. With the great success of Deep Neural Networks (DNNs)
in various fields, advanced works recently have proposed several DNN-based
models for CF, which have been proven effective. However, the neural networks
are all designed manually. As a consequence, it requires the designers to
develop expertise in both CF and DNNs, which limits the application of deep
learning methods in CF and the accuracy of recommended results. In this paper,
we introduce the genetic algorithm into the process of designing DNNs. By means
of genetic operations like crossover, mutation, and environmental selection
strategy, the architectures and the connection weights initialization of the
DNNs can be designed automatically. We conduct extensive experiments on two
benchmark datasets. The results demonstrate the proposed algorithm outperforms
several manually designed state-of-the-art neural networks.
