  Recent years have witnessed the great success of deep neural networks in many
research areas. The fundamental idea behind the design of most neural networks
is to learn similarity patterns from data for prediction and inference, which
lacks the ability of logical reasoning. However, the concrete ability of
logical reasoning is critical to many theoretical and practical problems. In
this paper, we propose Neural Logic Network (NLN), which is a dynamic neural
architecture that builds the computational graph according to input logical
expressions. It learns basic logical operations as neural modules, and conducts
propositional logical reasoning through the network for inference. Experiments
on simulated data show that NLN achieves significant performance on solving
logical equations. Further experiments on real-world data show that NLN
significantly outperforms state-of-the-art models on collaborative filtering
and personalized recommendation tasks.
